Partially View-aligned Clustering (PVC) addresses the challenge of partial view alignment in multi-view learning by leveraging complementary and consistent information. While existing PVC methods show promise, most rely on distance-based strategies that are sensitive to view-specific details and noise, limiting their robustness. In this work, we propose a novel view alignment strategy that reformulates the alignment task as an anomaly detection problem. Rather than learning a view-alignment matrix that enforces strict one-to-one correspondences across views, we adopt a progressive approach to identify well-aligned samples. Specifically, we sample subsets of data by generating random view combinations from unaligned samples and propose an anomaly combination detection module to evaluate the alignment consistency of these combinations. In addition, our progressive training framework alternates between updating model parameters and selecting high-confidence view combinations for subsequent optimization. By reformulating view alignment as an anomaly detection task, our approach provides a more robust and effective solution to partial view alignment. Experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches in the PVC problem.

Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single-cell RNA-seq and spatial transcriptomics benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene-expression signatures, further validating the biological relevance of our approach.

The development of single-cell RNA sequencing (scRNA-seq) technology has enabled the exploration of biological processes at the cellular level. A critical task in scRNA-seq data analysis is the unsupervised clustering of cells to distinguish different cell types. While various clustering methods have been successfully developed for scRNA-seq data, they still face limitations, particularly in terms of unstable clustering performance. This is often due to their inability to fully capture the intrinsic properties of cells, especially in the presence of high dropout rates and noise in the data. In this work, we propose a SEmantic-Aware contrastive Learning (SEAL) approach for scRNA-seq clustering. Specifically, we randomly mask the gene expression of each cell to generate two different augmentations of the cell data, and then apply semantic-aware contrastive learning to capture semantically invariant representations across these augmentations by leveraging semantic information from generated pseudo-labels. Experimental results demonstrate that our method effectively learns biologically meaningful representations and accurately identifies cell types.

Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in real-world scenarios, collecting strictly aligned views is challenging, and learning from both aligned and unaligned data becomes a more practical solution. Partially View-aligned Clustering (PVC) aims to learn correspondences between misaligned view samples to better exploit the potential consistency and complementarity across views, including both aligned and unaligned data. However, most existing PVC methods fail to leverage unaligned data to capture the shared semantics among samples from the same cluster. Moreover, the inherent heterogeneity of multi-view data induces distributional shifts in representations, leading to inaccuracies in establishing meaningful correspondences between cross-view latent features and, consequently, impairing learning effectiveness. To address these challenges, we propose a Semantic MAtching contRasTive learning model (SMART) for PVC. The main idea of our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning to fully exploit semantic relationships in both aligned and unaligned data. Specifically, we mitigate view distribution shifts by aligning cross-view covariance matrices, which enables the inference of a semantic graph for all data. Guided by the learned semantic graph, we further exploit semantic consistency across views through semantic matching contrastive learning. After the optimization of the above mechanisms, our model smoothly performs semantic matching for different view embeddings instead of the cumbersome view realignment, which enables the learned representations to enjoy richer category-level semantics and stronger robustness. Extensive experiments on eight benchmark datasets demonstrate that our method consistently outperforms existing approaches on the PVC problem. The code is available at https://github.com/THPengL/SMART.

Identifying cancer subtypes is crucial for understanding disease progression. With advancements in high-throughput experimental technology, leveraging multiple types of omic data for subtype identification has become feasible. Various integrative cancer subtyping methods present a promising computational approach for identifying cancer subtypes from heterogeneous datasets. While existing integrative cancer subtyping methods have shown promising results in this task, efficiently integrating and clustering multi-omics datasets remains challenging due to high noise levels in omics data, which hinder accurate relationship capture among samples. To overcome this challenge, we propose a new deep multi-view subspace clustering model that introduces a self-reinforced learning strategy. This strategy iteratively enhances the quality of self-representation, crucial for capturing relationships among samples and for clustering. Specifically, during model training, our method is capable of learning a highly reliable self-representation by leveraging a good neighbor learning approach. This capability enables us to capture more accurate and robust relationships among samples. Subsequently, with the assistance of this highly reliable self-representation, we further develop a learnable view-graph fusion approach, which enables us to learn an accurate consensus for clustering and guides the overall model learning process. Additionally, we introduce a local graph-guided learning mechanism based on an initial graph learned from raw data. This mechanism helps prevent the model from converging to suboptimal solutions, thereby avoiding unsatisfactory and unstable results. Experimental results demonstrate that our method outperforms several state-of-the-art methods, verify the effectiveness of our approach in cancer subtype identification task.

Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more serious in multi-domain scenarios. In this paper, we propose to extract multi-layer features from the PrLM for efficient training since the training process is independent to its large backbone. Meanwhile, compared with the conventional feature extraction, we leverage prompts to induce PrLM for generating sentiment-aware features which lead to significant improvement on the sentiment analysis. In addition, most previous methods adopted a domain alignment paradigm for multi-domain learning, which becomes cumbersome when the number of domains is large. Therefore, we propose a novel prompt-augmented cross-domain contrastive learning for generalizable performance, which clusters samples with the same label under different prompts or domains. Our method is evaluated on two public multi-domain sentiment analysis benchmarks, which significantly outperforms recent state-of-the-art methods. Extensive ablation studies also verify the effectiveness of each proposed component.

Multi-view clustering (MVC) has shown that leveraging both consistency and complementary information across views enhances clustering performance. However, most existing methods focus on aligning features into the same dimension, often neglecting cross-view heterogeneity and introducing discrepancies. To address this, we propose a novel multi-view clustering framework that combines cross-view neighborhood contrastive learning with a cross-attention view-mixup feature learning mechanism. Specifically, the cross-attention view-mixup module learns view-invariant feature representations by capturing complementary and consistent information, while the neighborhood contrastive learning module uncovers semantic structures across views based on the learned mixup features. By implicitly performing feature mixup across views and effectively integrating cross-view neighborhood contrastive learning, our method alleviates cross-view discrepancies and enables more effective integration of complementary and consistent information, ultimately enhancing clustering performance. Experiments conducted on several real datasets demonstrate the effectiveness of our proposed method in comparision with several representative MVC approaches.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell-type annotation. By transferring knowledge between scRNA-seq and ST data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets. RESULTS: In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments. AVAILABILITY AND IMPLEMENTATION: The COME is freely available in GitHub (https://github.com/cindyway/COME).

Single-cell RNA sequencing (scRNA-seq) technology has transformed gene expression studies by enabling analysis at the individual cell level, offering unprecedented insights into cellular heterogeneity. A key challenge in scRNA-seq data analysis is cell type identification, which requires grouping cells with similar gene expression profiles using unsupervised clustering methods. However, the high dimensionality, inherent noise, and significant sparsity of scRNA-seq data present substantial obstacles to accurately determining relationships among cell samples. To address these challenges, we propose a novel deep subspace clustering approach for cell type identification that captures a more reliable subspace structure from scRNA-seq data. Our method leverages a robust self-representation learning framework to effectively characterize and learn the underlying cluster structure. This framework is optimized through an integrated strategy combining a structure-guided approach with an optimal transport algorithm, enhancing the robustness of the subspace clustering process. By mitigating the effects of noise and sparsity in scRNA-seq data, this approach enables more accurate cell clustering. Experimental results on 18 real scRNA-seq datasets demonstrate that our method outperforms several state-of-the-art clustering approaches tailored for scRNA-seq data, excelling in both accuracy and interpretability.

Cancer subtyping is crucial for categorizing patients into distinct groups, enabling precision medicine and personalized therapies. As multi-omic analysis becomes more prevalent, integrating data from various omics provides deeper insights into the potential relationships between cancer subtypes. Although most cancer subtyping methods show promising performance, they have several limitations. These methods fail to account for omic differences, address noise in similarity matrices, and preserve the manifold structure of high-dimensional data in lowdimensional space. This study proposes a Robust Diverse Multiview Learning (RDML) model for cancer subtyping. Specifically, multi-view self-representation matrices are formulated as a thirdorder tensor. Differences between views are captured using an orthogonal diversity term, thereby reducing the redundant information between views. To enhance robustness of model to noise, we explicitly separate the self-representation tensor into a clean tensor and a noise tensor. Additionally, Laplacian manifold regularization is employed to preserve the local structure of highdimensional data in low-dimensional space. An efficient algorithm is designed to solve the proposed model. Comprehensive experiments are conducted on ten datasets, demonstrating the superior performance of the proposed model.

Multi-view clustering (MVC), which integrates information from multiple views to enhance performance, has garnered increasing attention in recent years. Partially View-aligned Clustering (PVC), which is a particularly critical aspect of this process, requires a thorough exploration of complementary and consistent information under conditions of partial view alignment. However, most existing PVC methods primarily focus on semantic consistency, employing semantic consistency features for both view alignment and clustering tasks. These methods neglect the effects of noise and complementary information across multiple views and the suitability of these features for clustering. To address these limitations, our approach aims to leverage three distinct types of consistency to extract semantic consistency features and clustering consistency features, which are specifically designed for view alignment and clustering tasks, respectively. By omitting the reconstruction process, we mitigate the adverse effects of mutual information and noise on view alignment. Specifically, we first exploit the structural consistency of similarity graphs across different views to guide feature extraction in view-specific autoencoders. This process produces structural consistency features that are both cluster-discriminative and structurally coherent. Subsequently, two separate multilayer perceptrons (MLPs) are trained via contrastive learning to extract semantic consistency features and clustering consistency features from the structural features. These features are optimized for their respective tasks. Ultimately, a self-paced style view alignment strategy is used to iteratively re-align the data based on semantic and clustering consistency while the model is optimized via the re-aligned data. Extensive experiments on multiple real-world benchmark datasets demonstrate that our method outperforms the state-of-the-art multi-view approaches, highlighting its effectiveness in tackling the challenges of PVC. The code is available at https://github.com/kongyiH/TCLPVC.

Graph-based multiview clustering (MVC) approaches have demonstrated impressive performance by leveraging the consistency properties of multiview data in an unsupervised manner. However, existing methods for graph learning heavily rely on either Euclidean structures or the manifold topological structures derived from fixed view-specific graphs. Unfortunately, these approaches may not accurately reflect the consensus topological structure in a multiview setting. To address this limitation and enhance the intrinsic graph learning process, an adaptive exploration of a more appropriate consistency topological structure is required. Toward this end, we propose a novel approach called collaborative topological graph learning (CTGL) for MVC. The key idea is to adaptively discover the consistent topological structure to guide intrinsic graph learning. We achieve this by introducing an auxiliary consistency graph that formulates the topological relevance learning function. However, estimating the auxiliary consistency graph is not straightforward, as it is based on the learned view-specific graphs and requires prior availability. To overcome this challenge, we develop a collaborative learning strategy that simultaneously learns both the auxiliary consistency graph and view-specific graphs using tensor learning techniques. This strategy enables the adaptive exploration of the consistency topological structure during graph learning, resulting in more accurate clustering outcomes. Extensive experiments are provided to show the effectiveness of the proposed method. The source code can be found at https://github.com/CLiu272/CTGL.

Face retouching is to beautify a face image, while preserving the image content as much as possible. It is a promising yet challenging task to remove face imperfections and fill with normal skin. Generic image enhancement methods are hampered by the lack of imperfection localization, which often results in incomplete removal of blemishes at large scales. To address this issue, we propose a transformer-based approach, RetouchFormer, which simultaneously identify imperfections and synthesize realistic content in the corresponding regions. Specifically, we learn a latent dictionary to capture the clean face priors, and predict the imperfection regions via a reconstruction-oriented localization module. Also based on this, we can realize face retouching by explicitly suppressing imperfections in our selective self-attention computation, such that local content will be synthesized from normal skin. On the other hand, multi-scale feature tokens lead to increased flexibility in dealing with the imperfections at various scales. The design elements bring greater effectiveness and efficiency. RetouchFormer outperforms the advanced face retouching methods and synthesizes clean face images with high fidelity in our list of extensive experiments performed.

Recently, fine-tuning the large pre-trained language models on the labeled sentiment dataset achieves appealing performance. However, the obtained model may not generalize well to the other domains due to the domain shift, and it is expensive to update the entire parameters within the large models. Although some existing domain matching methods are proposed to alleviate the above issues, there are multiple relevant source domains in practice which makes the whole training more costly and complicated. To this end, we focus on the efficient unsupervised multi-source sentiment adaptation task which is more challenging and beneficial for real-world applications. Specifically, we propose to extract multi-layer features from the large pre-trained model, and design a dynamic parameters fusion module to exploit these features for both efficient and adaptive tuning. Furthermore, we propose a novel feature structure matching constraint, which enforces similar feature-wise correlations across different domains. Compared with the traditional domain matching methods which tend to pull all feature instances close, we show that the proposed feature structure matching is more robust and generalizable in the multi-source scenario. Extensive experiments on several multi-source sentiment analysis benchmarks demonstrate the effectiveness and superiority of our proposed framework.

Blind face restoration focuses on restoring high-fidelity details from images subjected to complex and unknown degradations, while preserving identity information. In this paper, we present a Prior-based Latent Transformation approach (PLTrans), which is specifically designed to learn a degradation-unaware representation, thereby allowing the restoration network to effectively generalize to real-world degradation. Toward this end, PLTrans learns a degradation-unaware query via a latent diffusion-based regularization module. Furthermore, conditioned on the features of a degraded face image, a latent dictionary that captures the priors of HQ face images is leveraged to refine the features by mapping the top-d nearest elements. The refined version will be used to build key and value for the cross-attention computation, which is tailored to each degraded image and exhibits reduced sensitivity to different degradation factors. Conditioned on the resulting representation, we train a decoding network that synthesizes face images with authentic details and identity preservation. Through extensive experiments, we verify the effectiveness of the design elements and demonstrate the generalization ability of our proposed approach for both synthetic and unknown degradations. We finally demonstrate the applicability of PLTrans in other vision tasks.

Facial makeup transfer aims to replicate reference makeup on target face, and the existing methods are mainly based on a generic adversarial training process. In this work, we design a Reference-conditional Makeup-aware Discrimination approach (RcMD) to facilitate makeup transfer. Specifically, we perform region-wise semantic feature extraction from a reference makeup image and a source image without makeup. A generator learns to capture and render the reference makeup by modulating the region-wise intermediate features. To ensure precise makeup on target face, we incorporate a reference-conditional discrimination network, which learns to measure the regional makeup consistency between reference and synthesized images. Considering the discrepancy between reference and target faces, an alignment module is trained to fuse the extracted features, conditioned on the reference style. Based on the feature statistics, we perform regional real-synthesized makeup discrimination to ensure precise makeup rendering. Extensive experiments are performed to demonstrate the effectiveness of our designed modules and the superior performance of RcMD in transferring diverse real-world facial makeup.

The prevalence of multimedia applications has led to increased concerns and demand for auto face retouching. Face retouching aims to enhance portrait quality by removing blemishes. However, the existing auto-retouching methods rely heavily on a large amount of paired training samples, and perform less satisfactorily when handling complex and unusual blemishes. To address this issue, we propose a Language-guided Blemish Removal Transformer for automatically retouching face images, while at the same time reducing the dependency of the model on paired training data. Our model is referred to as LangBRT, which leverages vision-language pre-training for precise facial blemish removal. Specifically, we design a text-prompted blemish detection module that indicates the regions to be edited. The priors not only enable the transformer network to handle specific blemishes in certain areas, but also reduce the reliance on retouching training data. Further, we adopt a target-aware cross attention mechanism, such that the blemish-like regions are edited accurately while at the same time maintaining the normal skin regions unchanged. Finally, we adopt a regularization approach to encourage the semantic consistency between the synthesized image and the text description of the desired retouching outcome. Extensive experiments are performed to demonstrate the superior performance of LangBRT over competing auto-retouching methods in terms of dependency on training data, blemish detection accuracy and synthesis quality.

Partially View-aligned Clustering (PVC) presents a challenge as it requires a comprehensive exploration of complementary and consistent information in the presence of partial alignment of view data. Existing PVC methods typically learn view correspondence based on latent features that are expected to contain common semantic information. However, latent features obtained from heterogeneous spaces, along with the enforcement of alignment into the same feature dimension, can introduce cross-view discrepancies. In particular, partially view-aligned data lacks sufficient shared correspondences for the critical common semantic feature learning, resulting in inaccuracies in establishing meaningful correspondences between latent features across different views. While feature representations may differ across views, instance relationships within each view could potentially encode consistent common semantics across views. Motivated by this, our aim is to learn view correspondence based on graph distribution metrics that capture semantic view-invariant instance relationships. To achieve this, we utilize similarity graphs to depict instance relationships and learn view correspondence by aligning semantic similarity graphs through optimal transport with graph distribution. This facilitates the precise learning of view alignments, even in the presence of heterogeneous view-specific feature distortions. Furthermore, leveraging well-established cross-view correspondence, we introduce a cross-view contrastive learning to learn semantic features by exploiting consistency information. The resulting meaningful semantic features effectively isolate shared latent patterns, avoiding the inclusion of irrelevant private information. We conduct extensive experiments on several real datasets, demonstrating the effectiveness of our proposed method for the PVC task.

The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

Incomplete multi-view clustering (IMVC) presents a significant challenge due to the need for effectively exploring complementary and consistent information within the context of missing views. One promising strategy to tackle this challenge is to recover missing views by inferring the missing samples. However, such approaches often fail to fully utilize discriminative structural information or adequately address consistency, as it requires such information to be known or learnable in advance, which contradicts the incomplete data setting. In this study, we propose a novel approach calledLatentStructure-Aware view recovery (LaSA) for the IMVC task. Our objective is to recover missing views through discriminative latent representations by leveraging structural information. Specifically, our method offers a unified closed-form formulation that simultaneously performs missing data inference and latent representation learning, using a learned intrinsic graph as structural information. This formulation, incorporating graph structure information, enhances the inference of missing data while facilitating discriminative feature learning. Even when intrinsic graph is initially unknown due to incomplete data, our formulation allows for effective view recovery and intrinsic graph learning through an iterative optimization process. To further enhance performance, we introduce an iterative consistency diffusion process, which effectively leverages the consistency and complementary information across multiple views. Extensive experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

Great progress has been made in StyleGAN-based image editing. To associate with preset attributes, most existing approaches focus on supervised learning for semantically meaningful latent space traversal directions, and each manipulation step is typically determined for an individual attribute. To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image. Specifically, we adopt a two-stage architecture for a latent mapping network to break down the transformation process into two manageable steps. Our network first learns a diverse set of semantic directions tailored to an input image, and later nonlinearly fuses the ones associated with the target attributes to infer a residual vector. The resulting tightly interlinked two-stage architecture delivers the flexibility to handle diverse attribute combinations. By leveraging the cross-modal text-image representation of CLIP, we can perform pseudo annotations based on the semantic similarity between preset attribute text descriptions and training images, and further jointly train an auxiliary attribute classifier with the latent mapping network to provide semantic guidance. We perform extensive experiments to demonstrate that the adopted strategies contribute to the superior performance of TUSLT.

Cancer is a disease with a complex genome of altered functions.However, most existing driver gene identification approaches rarely consider driver genes may have the same functional properties.To overcome this issue, we propose the gamma distribution test for the driver gene identification based on similarity networks, termed GASN, which identifies driver genes by combining machine learning and distributional statistics methods.Similarity networks are able to learn gene similarities and key features that represent the functional impact of genes.In addition, we classify genes into different cellular compartments and use the gamma distribution test within cellular compartments to identify significant driver genes.The experimental results show that our method outperforms the other 17 comparative methods.

Incomplete multi-view clustering (IMVC) is challenging, as it requires adequately exploring complementary and consistency information under the incompleteness of data. Most existing approaches attempt to overcome the incompleteness at instance-level. In this work, we develop a new approach to facilitate IMVC from a new perspective. Specifically, we transfer the issue of missing instances to a similarity graph completion problem for incomplete views, and propose a self-supervised multi-view graph completion algorithm to infer the associated missing entries. Further, by incorporating constrained feature learning, the inferred graph can be naturally leveraged in representation learning. We theoretically show that our feature learning process performs an Auto-Regressive filter function by encoding the learned similarity graph, which could yield discriminative representation for a clustering task. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods.

Recently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation cost. To this end, we focus on multi-source unsupervised sentiment adaptation problem with the pre-trained features, which is more practical and challenging. We first design a dynamic feature network to fully exploit the extracted pre-trained features for efficient domain adaptation. Meanwhile, with the difference of the traditional source-target domain alignment methods, we propose a novel asymmetric mutual learning strategy, which can robustly estimate the pseudo-labels of the target domain with the knowledge from all the other source models. Experiments on multiple sentiment benchmarks show that our method outperforms the recent state-of-the-art approaches, and we also conduct extensive ablation studies to verify the effectiveness of each the proposed module.

Deep subspace clustering has achieved remarkable performance in unsupervised clustering tasks. The self-supervised approach is further introduced to learn more discriminative representation for enhancing clustering performance. Despite the significant improvement of clustering performance by exploiting self-supervision information, these approaches heavily depend on the high quality of pseudo-label from the current clustering result and this will inevitably degrade the clustering performance when the obtained pseudo-labels are incorrect. To solve this issue, we develop a robust self-supervised deep subspace clustering approach by exploiting the reliable self-supervised information during training. The proposed method is involved in two key steps: a diffusion processing step is developed to improve self-expressiveness matrix such that more accurate clustering result (pseudo-labels) can be obtained. More importantly, we further propose to estimate and exploit the reliability of the assigned pseudo-label for each sample to alleviate the negative impact of incorrect pseudo-labels, such that the unreliable self-supervision can be further alleviated. Experimental studies on several benchmark datasets validate the effectiveness of our approach in terms of refining the self-supervised information. The source code of the proposed method is available at the https://github.com/stuljj/RSDSC.git.

Deep subspace clustering methods have attracted extensive attention due to the great improvement in both representation ability and precision of non-linear data. However, the rich information which is contained in the self-expression matrix is unexplored since existing approaches use the self-expression matrix only as a tool for learning inter-sample relationships and clustering. In addition, such models treat outlier and noise points equally with other points, which inevitably degrades the clustering performance. To overcome these issues, we develop a progressive deep subspace clustering approach by extracting delayed fitting probabilities from the module and then use the probabilities to defer the fitting of unreliable points. Specifically, we calculate the probabilities that each sample lies in each subspace based on the results of the self-expression matrix and spectral clustering, and then estimate the reliability of the cluster assignment of each sample as delayed fitting probability to reweight the loss of each sample. Experiments on five benchmark datasets validate the effectiveness of the proposed method.

Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://github.com/CLiu272/S2GC).

Ensemble learning has many successful applications because of its effectiveness in boosting the predictive performance of classification models. In this article, we propose a semisupervised multiple choice learning (SemiMCL) approach to jointly train a network ensemble on partially labeled data. Our model mainly focuses on improving a labeled data assignment among the constituent networks and exploiting unlabeled data to capture domain-specific information, such that semisupervised classification can be effectively facilitated. Different from conventional multiple choice learning models, the constituent networks learn multiple tasks in the training process. Specifically, an auxiliary reconstruction task is included to learn domain-specific representation. For the purpose of performing implicit labeling on reliable unlabeled samples, we adopt a negative$\ell _{1}$-norm regularization when minimizing the conditional entropy with respect to the posterior probability distribution. Extensive experiments on multiple real-world datasets are conducted to verify the effectiveness and superiority of the proposed SemiMCL model.

In this paper, we propose two GAN-based enhanced deep subspace clustering approaches: deep subspace clustering via dual adversarial generative networks (DSC-DAG) and self-supervised deep subspace clustering with adversarial generative networks ($S^2 DSC-AG$). In DSC-DAG, the distributions of both the inputs and corresponding latent representations are learning via adversarial training simultaneously. Besides, there are two kinds of synthetical representations to facilitate the fine-tuning of the encoder module: the combinations of latent representations with certain random combination coefficients and the representations of real-like inputs derived from noise variables. In$S^DSC-AG$, a self-supervised information learning module substitutes for adversarial learning in the latent space, since both of them play the same role in learning discriminative latent representations. We analyze the connections between these methods and demonstrate their equivalences. We conduct extensive experiments on multiple real-world data sets against state-of-the-art subspace clustering methods in terms of accuracy, normalized mutual information and purity. Experimental results demonstrate the effectiveness and superiority of our proposed methods.

Recently, multitask learning has been successfully applied to survival analysis problems. A critical challenge in real-world survival analysis tasks is that not all instances and tasks are equally learnable. A survival analysis model can be improved when considering the complexities of instances and tasks during the model training. To this end, we propose an asymmetric graph-guided multitask learning approach with self-paced learning for survival analysis applications. The proposed model is able to improve the learning performance by identifying the complex structure among tasks and considering the complexities of training instances and tasks during the model training. Especially, by incorporating the self-paced learning strategy and asymmetric graph-guided regularization, the proposed model is able to learn the model in a progressive way from "easy" to "hard" loss function items. In addition, together with the self-paced learning function, the asymmetric graph-guided regularization allows the related knowledge transfer from one task to another in an asymmetric way. Consequently, the knowledge acquired from those earlier learned tasks can help to solve complex tasks effectively. The experimental results on both synthetic and real-world TCGA data suggest that the proposed method is indeed useful for improving survival analysis and achieves higher prediction accuracies than the previous state-of-the-art methods.

In this work, we propose an unsupervised ensemble learning method via network generation, referred to as UELNG. Specifically, we first generate weights of clustering ensemble models by adopting HyperGAN, and obtain diverse partitions for data. With these partitions, we can easily identify high-confident pseudo-labels as supervised information to predict low-entropy labels for unlabeled augmented data, thereby enhancing the quality of pseudo-labels and clustering accuracy. We conduct experiments on multiple data sets. Experimental results indicate that our method outperforms state-of-the-art methods by 0.3%, 1.8%, 5.7%, 3.2% and 2.4% on MNIST, STL-10, CIFAR-10, Reuters and 20News, respectively, which demonstrates the effectiveness of our proposed UELNG.

Both target-specific and domain-invariant features can facilitate Open Set Domain Adaptation (OSDA). To exploit these features, we propose a Knowledge Exchange (KnowEx) model which jointly trains two complementary constituent networks: (1) a Domain-Adversarial Network (DAdvNet) learning the domain-invariant representation, through which the supervision in source domain can be exploited to infer the class information of unlabeled target data; (2) a Private Network (PrivNet) exclusive for target domain, which is beneficial for discriminating between instances from known and unknown classes. The two constituent networks exchange training experience in the learning process. Toward this end, we exploit an adversarial perturbation process against DAdvNet to regularize PrivNet. This enhances the complementarity between the two networks. At the same time, we incorporate an adaptation layer into DAdvNet to address the unreliability of the PrivNet's experience. Therefore, DAdvNet and PrivNet are able to mutually reinforce each other during training. We have conducted thorough experiments on multiple standard benchmarks to verify the effectiveness and superiority of KnowEx in OSDA.

Multitask feature selection (MTFS) methods have become more important for many real world applications, especially in a high-dimensional setting. The most widely used assumption is that all tasks share the same features, and the l2,1 regularization method is usually applied. However, this assumption may not hold when the correlations among tasks are not obvious. Learning with unrelated tasks together may result in negative transfer and degrade the performance. In this paper, we present a flexible MTFS by graph-clustered feature sharing approach. To avoid the above limitation, we adopt a graph to represent the relevance among tasks instead of adopting a hard task set partition. Furthermore, we propose a graph-guided regularization approach such that the sparsity of the solution can be achieved on both the task level and the feature level, and a variant of the smooth proximal gradient method is developed to solve the corresponding optimization problem. An evaluation of the proposed method on multitask regression and multitask binary classification problem has been performed. Extensive experiments on synthetic datasets and real-world datasets demonstrate the effectiveness of the proposed approach to capture task structure.

Using an ensemble of neural networks with consistency regularization is effective for improving performance and stability of deep learning, compared to the case of a single network. In this paper, we present a semi-supervised Deep Coupled Ensemble (DCE) model, which contributes to ensemble learning and classification landmark exploration for better locating the final decision boundaries in the learnt latent space. First, multiple complementary consistency regularizations are integrated into our DCE model to enable the ensemble members to learn from each other and themselves, such that training experience from different sources can be shared and utilized during training. Second, in view of the possibility of producing incorrect predictions on a number of difficult instances, we adopt class-wise mean feature matching to explore important unlabeled instances as classification landmarks, on which the model predictions are more reliable. Minimizing the weighted conditional entropy on unlabeled data is able to force the final decision boundaries to move away from important training data points, which facilitates semi-supervised learning. Ensemble members could eventually have similar performance due to consistency regularization, and thus only one of these members is needed during the test stage, such that the efficiency of our model is the same as the non-ensemble case. Extensive experimental results demonstrate the superiority of our proposed DCE model over existing state-of-the-art semi-supervised learning methods.

Deep mutual learning jointly trains multiple essential networks having similar properties to improve semi-supervised classification. However, the commonly used consistency regularization between the outputs of the networks may not fully leverage the difference between them. In this paper, we explore how to capture the complementary information to enhance mutual learning. For this purpose, we propose a complementary correction network (CCN), built on top of the essential networks, to learn the mapping from the output of one essential network to the ground truth label, conditioned on the features learnt by another. To make the second essential network increasingly complementary to the first one, this network is supervised by the corrected predictions. As a result, minimizing the prediction divergence between the two complementary networks can lead to significant performance gains in semi-supervised learning. Our experimental results demonstrate that the proposed approach clearly improves mutual learning between essential networks, and achieves state-of-the-art results on multiple semi-supervised classification benchmarks. In particular, the test error rates are reduced from previous 21.23% and 14.65% to 12.05% and 10.37% on CIFAR-10 with 1000 and 2000 labels, respectively.

Autoencoders enjoy a remarkable ability to learn data representations. Research on autoencoders shows that the effectiveness of data interpolation can reflect the performance of representation learning. However, existing interpolation methods in autoencoders do not have enough capability of traversing a possible region between two datapoints on a data manifold, and the distribution of interpolated latent representations is not considered.To address these issues, we aim to fully exert the potential of data interpolation and further improve representation learning in autoencoders. Specifically, we propose the multidimensional interpolation to increase the capability of data interpolation by randomly setting interpolation coefficients for each dimension of latent representations. In addition, we regularize autoencoders in both the latent and the data spaces by imposing a prior on latent representations in the Maximum Mean Discrepancy (MMD) framework and encouraging generated datapoints to be realistic in the Generative Adversarial Network (GAN) framework. Compared to representative models, our proposed model has empirically shown that representation learning exhibits better performance on downstream tasks on multiple benchmarks.

In gene expression data analysis, the problems of cancer classification and gene selection are closely related. Successfully selecting informative genes will significantly improve the classification performance. To identify informative genes from a large number of candidate genes, various methods have been proposed. However, the gene expression data may include some important correlation structures, and some of the genes can be divided into different groups based on their biological pathways. Many existing methods do not take into consideration the exact correlation structure within the data. Therefore, from both the knowledge discovery and biological perspectives, an ideal gene selection method should take this structural information into account. Moreover, the better generalization performance can be obtained by discovering correlation structure within data. In order to discover structure information among data and improve learning performance, we propose a structured penalized logistic regression model which simultaneously performs feature selection and model learning for gene expression data analysis. An efficient coordinate descent algorithm has been developed to optimize the model. The numerical simulation studies demonstrate that our method is able to select the highly correlated features. In addition, the results from real gene expression datasets show that the proposed method performs competitively with respect to previous approaches.

The difference between sample distributions of public data sets and specific scenes can be very significant. As a result, the deployment of generic human detectors in real-world scenes most often leads to sub-optimal detection performance. To avoid the labor-intensive task of manual annotations, we propose a semi-supervised approach for training deep convolutional networks on partially labeled data. To exploit a large amount of unlabeled target data, the knowledge learnt from public data sets is transferred to new model training by adapting an auxiliary detector to the target scene. We hypothesize that the components of the auxiliary detector capture essential human characteristics useful for constructing a scene-adapted detector. A selective ensemble algorithm is proposed to select a subset of the components relevant to the target scene for recombination. The resulting model is applied for collecting high-confidence samples from unlabeled target data. Furthermore, a deep convolutional network is trained by progressively labeling and selecting new training samples in a self-paced way. The detailed experimental evaluation verifies the effectiveness and superiority of the proposed approach in scene-specific human detection.

With the development of social media applications, short text mining is becoming more and more important. Due to the sparseness of short text data, both the feature correlation information (word co-occurrence) and data contiguity information (context information) are less reliable, thus most existing text mining methods which are designed to address regular text data are less efficient in short text mining tasks. According to our observation from analysis of discriminative term distribution in short text data, we found that discriminative terms distribute in a non-uniform way among different domains, while background words have a tendency to distribute uniformly. This observation can be measured by a suitably defined functional of a term's probability distribution over different domains. In this paper, we adopt this distribution as the weight of terms to address the sparseness problem of short text data. We evaluate our method on two datasets, and experimental results show that our method outperforms previous approaches which require information infusion, and a number of state-of-the-art clustering algorithms. Furthermore, our method can obtain a more coherent clustering result.