Multi-view clustering aims to fully discover consistent clustering structures across different views, thereby improving the clustering performance. However, they usually lose structural information between different views during multi-view feature integration. Additionally, the misalignment of clustering centers from different views may lead to performance degradation. To tackle these challenges, in this paper, we propose a novel approach, called proxy-based dynamic view alignment with cross-view structure preservation (PDVA-CSP), for multi-view clustering. First, we design a graph aggregation module to explore the structure information between views through graph-based aggregation. Then we propose a proxy-based dynamic alignment strategy based on attention mechanisms to address the misalignment of proxies across views. Finally, we integrate them into an end-to-end learning framework, and then optimize it via a joint reconstruction loss and contrastive learning framework, seamlessly integrating feature extraction, view alignment, and clustering. The experimental results on several multi-view datasets demonstrate that the proposed PDVA-CSP method significantly outperforms other state-of-the-art methods in multi-view clustering tasks. The source code for this work will be available later.

Single-cell RNA sequencing (scRNA-seq) has provided unprecedented insights for life science research. In scRNA-seq data analysis, clustering is a crucial step that lays the foundation for downstream tasks. However, the high dimensionality and sparsity of scRNA-seq data lead to suboptimal representations learned by existing methods, thereby limiting clustering performance. To address these issues, we propose scDGCL, a novel dual-level and graph-constrained contrastive learning method for scRNA-seq data clustering. Specifically, we first design Dual-level Contrastive Learning (DCL), which optimizes cell representations by simultaneously considering similarities and disparities at both cell and cluster levels. Then, we design Graph-constrained Contrastive Learning (GCL), which aligns the DCL-derived representations with the graph's relational priors, further enhancing cell representations. To systematically evaluate scDGCL, we perform experiments on 12 real datasets and 8 simulated datasets. Comparing scDGCL with 17 representative clustering methods, the results demonstrate it's superiority in scRNA-seq data clustering. Ablation experiments and hyperparameter experiments are performed to verify the effectiveness of each component and the overall robustness of our method. Marker gene expression and cell trajectory inference analysis verify the biological plausibility of our method from a biological perspective.

Transformer-based methods have recently shown remarkable success in hyperspectral image classification (HSIC). However, their applications, in practice, still face two significant challenges. First, although the multihead mechanism in self-attention improves model robustness during training, it may overlook the continuity of spectral bands. Second, existing methods often struggle to effectively balance global and local information during multiscale feature extraction, limiting further improvements in classification performance. To address these issues, we propose a novel spectral-guided multiscale feature-aware Transformer (SMFAT) framework for HSIC. Specifically, a global low-rank spectral learning (GLSL) module is introduced to project hyperspectral image patches into a low-rank subspace, reducing spectral redundancy and capturing global spectral correlations. Furthermore, we introduce the multiscale feature-aware self-attention (MFASA) mechanism, which dynamically integrates fine- and coarse-grained features to enhance multiscale feature modeling. Finally, a spectral-guided fusion (SGF) module leverages the global spectral information extracted by the GLSL module to guide MFASA in more effectively capturing interspectral correlations and spectral continuity. This approach facilitates a more effective integration of spectral and spatial features in HSIs. Experiments on three well-known HSI datasets verify that the proposed SMFAT method significantly outperforms several state-of-the-art approaches in real-world HSIC tasks. The source code for this work is available at https://github.com/stellaZ77/SMFAT.

Multi-view document clustering (MvDC) aims to improve the accuracy and robustness of clustering by fully considering the complementarity of different views. However, in real-world clustering applications, most existing works suffer from the following challenges: 1) They primarily align multi-view data based on a single perspective, such as features and classes, thus ignoring the diversity and comprehensiveness of representations. 2) They treat each instance equally in cross-view contrastive learning without considering ambiguous ones, which weakens the model's discriminative ability. To address these problems, we propose an ambiguous instance-aware contrastive network with multi-level matching (AICN-MLM) for MvDC tasks. This model contains two key modules: a multi-level matching module and an ambiguous instance-aware contrastive learning module. The former attempts to align multi-view data from different perspectives, including features, pseudo-labels, and prototypes. The latter dynamically adjusts instance weights through a weight modulation function to highlight ambiguous instance pairs. Thus, our proposed method can effectively explore the consistency of multi-view document data and focus on ambiguous instances to enhance the model's discriminative ability. Extensive experimental results on several multi-view document datasets verify the effectiveness of our proposed method.

Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clustering, the dependencies between cells expand exponentially with the number of layers. This results in high computational complexity, negatively impacting the model's training efficiency. To address these challenges, we propose a novel approach, called decoupled GNNs, based on multi-view contrastive learning (scDeGNN), for scRNA-seq data clustering. Firstly, this method constructs two adjacency matrices to generate distinct views, and trains them using decoupled GNNs to derive the initial cell feature representations. These representations are then refined through a multilayer perceptron and a contrastive learning layer, ensuring the consistency and discriminability of the learned features. Finally, the learned representations are fused and applied to the cell clustering task. Extensive experimental results on nine real scRNA-seq datasets from various organisms and tissues show that the proposed scDeGNN method significantly outperforms other state-of-the-art scRNA-seq data clustering algorithms across multiple evaluation metrics.

Multilingual document clustering (MDC) aims to partition multilingual documents into distinct clusters based on topic categories in an unsupervised manner. However, existing MDC methods still suffer from several limitations in practice tasks. Firstly, most of them optimize multiple objectives within the same feature space, thereby leading to the conflict between learning consistently shared semantics and reconstructing inconsistent view-specific information. Secondly, several methods directly integrate information from multilingual documents during the fusion stage, thereby overlooking the semantic differences between different language features. To address the aforementioned problems, we propose a novel multi-view learning method, called Semantic Feature Graph Consistency with Contrastive Cluster Assignments (SFGC 3 A), for MDC. Specifically, the proposed SFGC 3 A method implements consistency objective and reconstruction objective in different feature spaces, thus effectively avoiding conflicts between consistency learning and inconsistency reconstruction. Subsequently, we design the semantic feature graph consistency and semantic label consistency modules to further explore consistent semantic information among multilingual documents, thereby reducing the semantic differences among different language views. Extensive experiments on several multilingual document datasets have shown the effectiveness of the proposed SFGC 3 A method in MDC tasks. The source codes for this work will be released later.

Deep hashing technology has recently become an essential tool for cross-modal retrieval on large-scale datasets. However, their performances heavily depend on accurate annotations to train the hashing model. In real applications, we usually only obtain low-quality label annotations owing to labor and time consumption limitations. To mitigate the performance degradation caused by noisy labels, in this paper, we propose a robust deep hashing method, called deep hashing with ranking learning (DHRL), for cross-modal retrieval. The proposed DHRL method consists of a refined semantic concept alignment module and a ranking-swapping module. In this first module, we adopt two transformers to perform the semantic alignment tasks between different modalities on a set of refined concepts, and then convert them into hash codes to reduce heterogeneous differences between multimodalities. The second module first identifies the noisy labels in the training set and ranks them according to ranking loss. Then it swaps the ranking information of different modal network branches. Unlike existing robust hashing methods for assuming noise distribution, our proposed DHRL method requires no prior assumptions for the input data. Extensive experiments on three benchmark datasets have shown that our proposed DHRL method has stronger advantages over other state-of-the-art hashing methods.

Zero-shot sketch-based image retrieval (ZS-SBIR) aims to utilize freehand sketches for retrieving natural images with similar semantics in realistic zero-shot scenarios. Existing works focus on zero-shot semantic transfer using category word embedding and leveraging teacher-student networks to alleviate catastrophic forgetting of pre-trained models. They aim to retain rich discriminative features to achieve zero-shot semantic transfer. However, the category word embedding method is insufficient in flexibility, thereby limiting their retrieval performances in ZS-SBIR scenarios. In addition, the teacher network used for generating guidance signals results in computational redundancy, requiring repeated processing of mini-batch inputs. To address these issues, we propose a deep residual coupled prompt learning (DRCPL) for ZS-SBIR. Specifically, we leverage the text encoder of CLIP to generate category classification weights, thereby improving the flexibility and generality of zero-shot semantic transfer. To tune text and vision representations effectively, we introduce learnable prompts at the input and freeze the parameters of the CLIP encoder. This approach not only effectively prevents catastrophic forgetting, but also significantly reduces the computational complexity of the model. We also introduce the text-vision prompt coupling function to enhance the coordinated consistency between the text and vision representations, ensuring that the two branches can train collaboratively. Finally, we gradually establish stage feature relationships by learning prompts independently at different early stages to facilitate rich contextual learning. Comprehensive experimental results demonstrate that our DRCPL method achieves state-of-the-art performance in ZS-SBIR tasks.

Clustering is a crucial step in single-cell RNA sequencing (scRNA-seq) data analysis, facilitating the discovery of new cell types and the grouping of similar cells. Recently, graph convolutional networks (GCNs) have gained prominence in scRNA-seq data clustering because they effectively learn cell representations by capturing the relationship between cells. However, GCNs are sensitive to noise in scRNA-seq data and are prone to over-smoothing, resulting in the loss of cell-specific information. To overcome these challenges, we propose sigRGCN, a robust residual graph convolutional network for scRNA-seq data clustering. Specifically, we first construct a disturbed cell graph by injecting noise into a cell graph constructed from scRNA-seq data. Then, we design a graph structure optimization graph convolutional network to eliminate the impact of noise in the disturbed cell graph. It significantly improves the robustness of the proposed model in real scRNA-seq data clustering tasks. After that, we utilize a $L$-layers residual graph convolutional network to alleviate the over-smoothing problem. It allows our model to effectively capture higher-order relationships between cells, leading to better cell representations. Finally, we employ a self-supervised manner to optimize our model. The experimental results on nine real scRNA-seq datasets show that our proposed model demonstrates competitive performance in real clustering tasks.

Recently, advances in single-cell sequencing (scRNAseq) technology have enabled large-scale transcriptome analysis with high efficiency and single-cell resolution. Clustering in scRNA-seq is crucial for revealing and categorizing new cell types and gene expression patterns. However, accurate cell clustering remains a challenge due to the high dimensionality and complexity of scRNA-seq data. To overcome this challenge, in this paper, we propose a novel deep scRNA-seq clustering framework, called sparse autoencoders with gene graph embedding (scSAG2E). In our scSAG2E method, two autoencoders are firstly used to learn the low-dimensional representation of cells and genes, respectively, and the gene expression matrix of cells is reconstructed by matrix multiplication. To preserve the manifold structure of cells, we incorporate graph regularization into the cell autoencoder. Additionally, we impose sparse constraints to address the sparsity of the gene expression matrix effectively. Meanwhile, we construct the gene graph using the KNN algorithm and then feed it into the graph convolution networks (GCNs). Therefore, it effectively captures the underlying structure among genes and enhances the signal of differentially expressed genes, leading to a more accurate representation of scRNA-seq data. Extensive experimental results on several publicly available scRNA-seq datasets show that the proposed scSAG2E method outperforms several state-of-the-art single-cell analysis methods in clustering tasks. The source code for this work has been released on https://github.com/xm0312/SCAG2E

Recent hyperspectral image (HSI) classification works have focused on developing a promising architecture by combining convolutional neural networks (CNNs) with Transformers. However, most of them fail to consider the interactive fusion of global and local features, thus significantly limiting the HSI classification performance. To address this problem, in this article, we propose a dual feature aggregation network (DFAN) for HSI classification tasks. It effectively aggregates local and global spatial-spectral features to achieve efficient classification. Specifically, we design a dual feature aggregation (DFA) module to extract and aggregate local and global spatial-spectral features. In this module, the lightweight local block is responsible for extracting local spatial-spectral features, and the global block is used to extract global spatial-spectral features and aggregation tasks. Concretely, the token local aggregation multilayer perceptron (TLA-MLP) module in the global block is to extract spatial-position-aware and spectral-channel-aware global discriminative information. Meanwhile, it learns multiscale neighboring token features by aggregating token local neighborhood features. In addition, we employ the local self-global aggregation block to learn the global aggregation features and then fuse them with the local spatial-spectral features. Afterward, the cross-attention aggregation of local and global spatial-spectral features is used to further improve the classification ability of our proposed model. The experimental results on three benchmark datasets show that our proposed DFAN method outperforms other state-of-the-art HSI classification methods.

The interaction between SNPs plays a key role in revealing the genetic mechanisms of complex diseases. However, as the order of SNP interactions increases, the number of SNP combinations increases exponentially, presenting a serious combinatorial explosion problem. Although swarm intelligence algorithms can alleviate the combinatorial explosion problem by optimizing search paths and have been widely used in SNP interaction detection, the performance of swarm intelligence algorithms is greatly affected by initialization and search direction. To mitigate these issues, we propose a high-order SNP interaction detection algorithm based on hierarchical clustering and multitask harmony search (abbreviated as HCMHS). In harmony memory initialization, considering that similar SNPs are more likely to form pathogenic combinations, hierarchical clustering is first used to cluster SNPs into different clusters, and then based on these clusters, harmony memory is initialized. In harmony search, considering the correlation between different orders of SNP interactions, the search directions for SNPs of various orders are optimized through a multi-task framework. To verify the performance of HCMHS, we carried out experiments on 58 simulated datasets and one real dataset, and HCMHS achieved the best results compared to nine advanced algorithms. The code of HCMHS is available at https://github.com/huoluan17-tian/HCMHS.

Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.

Cross-modal hashing retrieval approaches have received extensive attention owing to their storage superiority and retrieval efficiency. To achieve better retrieval performances, hashing methods seek to embed more semantic information of multi-modal data into hash codes. Existing deep cross-modal hashing methods typically learn hash functions from the similarity of paired data to generate hash codes. However, such locally-oriented learning methods often suffer from low efficiency and incomplete acquisition of semantic information. To address these challenges, this paper presents a novel deep hashing approach, called Proxy-based Graph Convolutional Hashing (PGCH), for cross-modal retrieval. Specifically, we use global similarity to construct proxy hash codes for two different modalities. This strategy of these proxy hash codes ensures that they include data points with significant distribution differences. It helps to match data from different modalities to different proxy hash codes, which can capture the global similarity of multi-modal hash codes and improve the efficiency of hash code learning. Subsequently, we employ a multi-modal contrastive loss to learn the global similarity. Furthermore, by constructing a proxy hash matrix from the proxy hash codes, we apply graph convolution to efficiently narrow the gap between different modalities, leading to a substantial improvement in retrieval performance for cross-modal retrieval tasks. The comprehensive experiments on four benchmark multimedia datasets demonstrate that our PGCH approach achieves better retrieval performances than a bundle of state-of-the-art hashing approaches.

Recently, low‐rank representation (LRR)‐based techniques have manifested remarkable results for data representation. To exploit the latent manifold structure of data, the graph regulariser is incorporated into the model of LRR. However, it is critical to construct an appropriate graph model and set the corresponding parameters. In addition, this procedure is usually time‐consuming and proved to be overfitting when using cross validation or discrete grid search. Two novel LRR‐based methods, called multiple graph regularised LRR and multiple hypergraph regularised LLR, are proposed to represent the high‐dimensional data. To guarantee the smoothness along the estimated manifold, the multiple graph regulariser and the multiple hypergraph regulariser are incorporated into the traditional LRR method, respectively, which results in a unified framework. Moreover, the augmented Lagrange multiplier is adopted to solve the proposed models. Extensive experiments on real image datasets show the effectiveness of the proposed methods.

Hundreds of narrow contiguous spectral bands collected by a hyperspectral sensor has provided the opportunity to identify the various materials present on the surface. Spatial information, that means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, by decomposing each pixel and the spatial neighborhood into a low-rank form, the spatial information can be efficiently integrated into the spectral signatures. Meanwhile, in order to describe the low-rank structure of the decomposed data more precisely, an ℓ1/2norm regularization is introduced and a discrete algorithm is proposed to solve the combined optimization problem. Experimental results on real hyperspectral data have demonstrated the effectiveness and versatility of the proposed spatial information-fused approach for hyperspectral imagery classification.

Given a hyperspectral image, unmixing tries to estimate the spectral responses of the latent constituent materials and their corresponding fractions. Recently, Nonnegative Matrix Factorization (NMF) has been widely applied to solve the hyper-spectral unmixing problem because of its plausible physical interpretation. In this paper, we propose a novel method, Multilayer Manifold and Sparsity constrained Nonnegative Matrix Factorization (MMSNMF), for hyperspectral unmixing. In this approach, Multilayer NMF decomposes a hyperspectral image iteratively at several layers. In order to consider both the manifold structure of hyperspectral image and the sparsity of abundance matrix, we impose a graph regularization term and a sparsity regularization term on both the spectral signature matrix and the abundance matrix. Experimental results on both synthetic and real data validate the effectiveness of the proposed method in hyperspectral unmixing.