Recommendation systems alleviate the issue of information overload via modeling user preferences from interaction sequences. Although self-attention based sequential models effectively capture long-range dependencies, they are susceptible to noise amplification in sparse sequences and over-smoothing of item representations, which obscures true user intent and reduces sensitivity to fine-grained behavioral changes. To overcome these challenges, we propose SAFA, a sparse sequential recommendation framework comprising: (1) an adaptive sparse attention mechanism that suppresses noisy interactions while preserving embedding diversity; (2) a frequency-aware encoder that decomposes interaction sequences into low-frequency components for long-term preference modeling and high-frequency components for short-term intent dynamics; and (3) a simplified focal loss that removes the class-balancing term while preserving the focusing factor, emphasizing hard-to-predict samples rather than class priors. Experiments on seven benchmark datasets demonstrate that SAFA consistently achieve state-of-the-art performance with average improvements of up to 3.77%, 4.10% and 4.25% in terms of HR@5, HR@10 and HR@20, respectively, and 4.30%, 4.78% and 4.58% in terms of NDCG@5, NDCG@10 and NDCG@20, respectively, over the best competing model. Ablation studies verify the importance of each component, with notable performance degradation upon removing the sparse attention or frequency-aware encoder. Overall, SAFA enhances sequential recommendation by improving robustness and discriminative learning under noisy and sparse conditions.

Collaborative filtering-based recommendation systems often face challenges such as data sparsity and cold start problem. Knowledge graphs have been leveraged as auxiliary information to mitigate these issues. However, current knowledge graph-based recommendation systems still struggle to accurately capture the latent features of users and items in the embedding space. In this article, we introduce a heterogeneous propagation-based recommendation framework that effectively integrates collaborative patterns derived from user–item interactions with knowledge graph data. Specifically, we first design hierarchical layerwise attention mechanisms (LAMs) tailored to the different layers of the knowledge graph propagation process. These mechanisms assign appropriate attention scores to feature representations at various levels of complexity, thereby enhancing the integration. Additionally, the feature learning is further improved by adding Gaussian noise to the initial latent features of users and items, which are then refined using a denoising autoencoder. This method helps learn more robust and discriminative representations for users and items, enhancing recommendation performance. Extensive experiments were conducted on the MovieLens-1M, Book-Crossing, and Last.FM datasets demonstrate that the proposed algorithm significantly outperforms eighteen state-of-the-art baselines. Furthermore, ablation studies confirm the effectiveness of each module, emphasizing their individual contributions to the overall performance improvement.

Ensemble learning improves the capability of convolutional neural network (CNN)-based discriminators, whose performance is crucial to the quality of generated samples in generative adversarial network (GAN). However, this learning strategy results in a significant increase in the number of parameters along with computational overhead. Meanwhile, the suitable number of discriminators required to enhance GAN performance is still being investigated. To mitigate these issues, we propose an evidential discriminator for GAN (EviD-GAN)-code is available at https://github.com/Tohokantche/EviD-GAN-to learn both the model (epistemic) and data (aleatoric) uncertainties. Specifically, by analyzing three GAN models, the relation between the distribution of discriminator's output and the generator performance has been discovered yielding a general formulation of GAN framework. With the above analysis, the evidential discriminator learns the degree of aleatoric and epistemic uncertainties via imposing a higher order distribution constraint over the likelihood as expressed in the discriminator's output. This constraint can learn an ensemble of likelihood functions corresponding to an infinite set of discriminators. Thus, EviD-GAN aggregates knowledge through the ensemble learning of discriminator that allows the generator to benefit from an informative gradient flow at a negligible computational cost. Furthermore, inspired by the gradient direction in maximum mean discrepancy (MMD)-repulsive GAN, we design an asymmetric regularization scheme for EviD-GAN. Unlike MMD-repulsive GAN that performs at the distribution level, our regularization scheme is based on a pairwise loss function, performs at the sample level, and is characterized by an asymmetric behavior during the training of generator and discriminator. Experimental results show that the proposed evidential discriminator is cost-effective, consistently improves GAN in terms of Frechet inception distance (FID) and inception score (IS), and performs better than other competing models that use multiple discriminators.

Despite the prevalence of reconstruction-based deep learning methods, time-series anomaly detection (TSAD) remains a tremendous challenge. Existing approaches often struggle with limited temporal contexts, insufficient representation of normal patterns, and flawed evaluation metrics, all of which hinder their effectiveness in detecting anomalous behavior. To address these issues, we introduce a simple dissimilarity-based approach for time-series anomaly detection (SimAD). Specifically, SimAD first incorporates a patching-based feature extractor capable of processing extended temporal windows and employs the EmbedPatch encoder to fully integrate normal behavioral patterns. Second, we design an innovative ContrastFusion module in SimAD, which strengthens the robustness of anomaly detection by highlighting the distributional differences between normal and abnormal data. Third, we introduce two robust enhanced evaluation metrics, unbiased affiliation (UAff) and normalized affiliation (NAff), designed to overcome the limitations of existing metrics by providing better distinctiveness and semantic clarity. The reliability of these two metrics has been demonstrated by both theoretical and experimental analyses. Experiments conducted on seven diverse time-series datasets clearly demonstrate SimAD's superior performance compared with state-of-the-art (SOTA) methods, achieving relative improvements of 19.85% on ${F}1$ , 4.44% on Aff-F1, 77.79% on NAff-F1, and 9.69% on AUC on six multivariate datasets. Code and pretrained models are available at https://github.com/EmorZz1G/SimAD.

Clustering methods achieve performance improvement by jointly learning representation and cluster assignment. However, they do not consider the confidence of pseudo-labels which are not optimal as supervised information, resulting into error accumulation. To address this issue, we propose a Robust Pseudo-labeling for Semantic Clustering (RPSC) approach, which includes two stages. In the first stage (RPSC-Self), we design a semantic pseudo-labeling scheme by using the consistency of samples, i.e., samples with same semantics should be close to each other in the embedding space. To exploit robust semantic pseudo-labels for self-supervised learning, we propose a soft contrastive loss (SCL) which encourage the model to believe high-confidence sematic pseudo-labels and be less driven by low-confidence pseudo-labels. In the second stage (RPSC-Semi), we first determine the semantic pseudo-label of a sample based on the distance between itself and cluster centers, followed by screening out reliable semantic pseudo-label by exploiting the consistency. These reliable pseudo-labels are used as supervised information in the pseudo-semi-supervised learning algorithm to further improve the performance. Experimental results show that RPSC outperforms 18 competitive clustering algorithms significantly on six challenging image benchmarks. In particular, RPSC achieves an accuracy of 0.688 on ImageNet-Dogs, which is an up to 24% improvement, compared with the second-best method. Meanwhile, we conduct ablation studies to investigate effects of different augmented strategies on RPSC as well as contributions of terms in SCL to clustering performance. Besides, experimental results indicate that SCL can be easily integrated into existing clustering methods and bring performance improvement.

Broad learning system (BLS) is a simple yet efficient learning algorithm that only needs to train a three-layer feedforward neural network. Although various BLS variants have been designed for supervised learning, none have been used for unsupervised learning. This paper proposes BLS-AE, a novel data clustering scheme that seamlessly combines BLS and auto-encoder. Then, graph regularization is introduced into BLS-AE to increase the capability of learning intrinsic structures in data and adaptation to various data simultaneously, which is termed BLSg-AE. Moreover, different concatenation styles of feature and enhancement nodes are investigated for reusing the learned features, followed by designing two special strategies (i.e., pruning optimization and incremental learning) to reduce the parameter scale significantly and improve performance, which is termed xBLSg-AE. To address the performance instability issue caused by random subspace in a single xBLSg-AE, the x-cascade broad learning system graph regularization multi-auto-encoder (xBLSg-MAE) algorithm is proposed. Extensive experiments are conducted on multiple real data sets to demonstrate that the proposed methods are more effective and robust than competing approaches.

High-dimensional small-size data seriously affects the performance of classifiers. By combining classifiers, ensemble learning obtains higher accuracy and more robust predictions. However, these classifier ensemble methods suffer from several limitations: 1) ensemble with sample space suffers from noise and redundant features; 2) constructing sample subspace on small-size data leads to an insufficient description of sample space; 3) ensemble with feature space leads to information loss, which will degrade performance of classifiers. To overcome the above limitations, a new classifier ensemble method based on subspace enhancement (CESE) is proposed for high-dimensional data classification. First, a superior subspace enhancement scheme (SSE) is designed to effectively implement feature selection and transformation for high-dimensional data, followed by generating multiple superior feature subspaces with diversity and discrimination, which enhances the representative ability of features. Second, we develop a mixed space enhancement process (MSE) based on multiscale rotation reconstruction and various subspace enhanced features of SSE. Furthermore, to improve the capacity of our method, we design various feature combination strategies for enhanced features from both SSE and MSE. Comparative results on 33 high-dimensional data sets indicate that our approach CESE outperforms different mainstream integrated system

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).

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.

Text classification has been widely explored in natural language processing. In this article, we propose a novel adaptive dense ensemble model (AdaDEM) for text classification, which includes local ensemble stage (LES) and global dense ensemble stage (GDES). To strengthen the classification ability and robustness of the enhanced layer, we propose a selective ensemble model based on enhanced attention convolutional neural networks (EnCNNs). To increase the diversity of the ensemble system, these EnCNNs are generated by using two manners: 1) different sample subsets and 2) different granularity kernels. Then, an evaluation criterion that considers both accuracy and diversity is proposed in LES to obtain effective integration results. Furthermore, to make better use of information flow, we develop an adaptive dense ensemble structure with multiple enhanced layers in GDES to mitigate the issue that there may be redundant or invalid enhanced layers in the cascade structure. We conducted extensive experiments against state-of-the-art methods on multiple real-world datasets, including long and short texts, which has verified the effectiveness and generality of our method.

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.

Broad learning system (BLS) is a novel and efficient model, which facilitates representation learning and classification by concatenating feature nodes and enhancement nodes. In spite of the efficient properties, BLS is still suboptimal when facing with imbalance problem. Besides, outliers and noises in imbalanced data remain a challenge for BLS. To address the above issues, in this paper we first propose a weighted BLS, which assigns a weight to each training sample, and adopt a general weighting scheme, which augments the weight of samples from the minority class. To further explore the prior distribution of original data, we design a density based weight generation mechanism to guide the specific weight matrix generation and propose the adaptive weighted broad learning system (AWBLS). This mechanism considers the inter-class and intra-class distance simultaneously in the density calculation. Finally, we propose the incremental weighted ensemble broad learning system (IWEB) by utilizing a progressive mechanism to further improve the stability and robustness of AWBLS. Extensive comparative experiments on 38 real-world data sets verfy that IWEB outperforms most of the imbalance ensemble classification methods.

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.

Graph-based methods have achieved impressive performance on semisupervised classification (SSC). Traditional graph-based methods have two main drawbacks. First, the graph is predefined before training a classifier, which does not leverage the interactions between the classifier training and similarity matrix learning. Second, when handling high-dimensional data with noisy or redundant features, the graph constructed in the original input space is actually unsuitable and may lead to poor performance. In this article, we propose an SSC method with novel graph construction (SSC-NGC), in which the similarity matrix is optimized in both label space and an additional subspace to get a better and more robust result than in original data space. Furthermore, to obtain a high-quality subspace, we learn the projection matrix of the additional subspace by preserving the local and global structure of the data. Finally, we intergrade the classifier training, the graph construction, and the subspace learning into a unified framework. With this framework, the classifier parameters, similarity matrix, and projection matrix of subspace are adaptively learned in an iterative scheme to obtain an optimal joint result. We conduct extensive comparative experiments against state-of-the-art methods over multiple real-world data sets. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.

The class imbalance problem has posed a leading challenge in real-world applications. Traditional methods focus on either the data level or algorithm level to solve the binary classification problem on imbalanced data, and seldom consider searching an effective transformation for classification. Besides, the undersampling process adopted in them is always subjective and unilateral. To address the above issues, we first propose a hybrid classifier ensemble (HCE) framework to conduct binary imbalanced data classification, which mainly includes a metric-based data space transformation (MDST) and an adaptive two-stage undersampling process (ATUP). The MDST aims to find a more appropriate embedding space for original imbalance data sets, and the ATUP considers both informative and representative samples to generate balanced data sets. Furthermore, we design a progressive HCE (PHCE) framework to improve the performance of HCE by utilizing a progressive mechanism with local and global evaluation criteria to select ensemble members. Extensive comparative experiments conducted on 28 real-world data sets exhibit that our method PHCE outperforms the majority of imbalance ensemble classification approaches.

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.

Healthcare question answering (HQA) system plays a vital role in encouraging patients to inquire for professional consultation. However, there are some challenging factors in learning and representing the question corpus of HQA datasets, such as high dimensionality, sparseness, noise, nonprofessional expression, etc. To address these issues, we propose an inception convolutional autoencoder model for Chinese healthcare question clustering (ICAHC). First, we select a set of kernels with different sizes using convolutional autoencoder networks to explore both the diversity and quality in the clustering ensemble. Thus, these kernels encourage to capture diverse representations. Second, we design four ensemble operators to merge representations based on whether they are independent, and input them into the encoder using different skip connections. Third, it maps features from the encoder into a lower-dimensional space, followed by clustering. We conduct comparative experiments against other clustering algorithms on a Chinese healthcare dataset. Experimental results show the effectiveness of ICAHC in discovering better clustering solutions. The results can be used in the prediction of patients' conditions and the development of an automatic HQA system.

Classifying high-dimensional small-size data is challenging in the field of pattern recognition. Traditional ensemble learning methods have several limitations: 1) sample-space based methods are easily affected by noise and redundant features; 2) feature-space based methods cannot excavate the essential characteristics of features; 3) feature subspaces cause information loss, which leads to a decline in accuracy; 4) most selective ensemble methods only consider the diversity and performance of sub-classifiers and ignore the impact on integration systems. To address the above limitations, we propose an adaptive classifier ensemble learning method (AdaSPEL) based on spatial perception for high-dimensional data. First, we design a local-space perception method for feature transformation, which encourages both high performance and diversity of the ensemble members. Second, we design a cross-space perception method based on the distribution of samples to obtain the cross-space enhanced features to provide a macro analysis for the characteristics of data. Furthermore, an adaptive selective ensemble method based on local and global evaluation mechanisms is proposed, which considers the impact of sub-classifiers on integrated systems. Experimental results on 33 high-dimensional data sets verify that our method outperforms mainstream ensemble learning methods based on feature space and sample space, and neural network-based algorithms.

Class-conditional generative models have gained popularity due to their characteristics of learning disentangled representations. However, these models typically require labeled examples in training. In this paper, we explore the feasibility of training these models on completely unlabeled data, under the assumption that we have access to other labeled data. The labeled data share the same label space, while their domain is shifted. Our model, which we refer to as KTransGAN, incorporates a classifier to transfer knowledge from the labeled data and performs collaborative learning with the conditional generator. By adopting these measures, KTransGAN is able to approximate the conditional distribution of the unlabeled data and simultaneously introduces a new solution to the unsupervised domain adaptation problem. To mitigate the training difficulty of our generative adversarial networks-based model, variational encoding and feature matching are also considered. From the empirical results, KTransGAN exhibits outstanding performance on a number of synthetic datasets and multiple real-world benchmarks. The quality of the synthesized instances is far superior to the pure variational autoencoding model. For example, on the CIFAR-10 dataset, our model scores 35.3 in FID, while the other model scores 128.45. In addition, the synthesis quality is close to the case when the model is trained in a fully supervised setting over the same number of training iterations. Regarding the classification performance, for instance, our model surpasses the highest state-of-the-art results (89.19%) by a large margin and achieves a test accuracy of 95.31% on the unlabeled data SVHN, while MNIST represents the labeled data. These results highlight the effectiveness of our proposed framework.

Semisupervised clustering methods improve performance by randomly selecting pairwise constraints, which may lead to redundancy and instability. In this context, active clustering is proposed to maximize the efficacy of annotations by effectively using pairwise constraints. However, existing methods lack an overall consideration of the querying criteria and repeatedly run semisupervised clustering to update labels. In this work, we first propose an active density peak (ADP) clustering algorithm that considers both representativeness and informativeness. Representative instances are selected to capture data patterns, while informative instances are queried to reduce the uncertainty of clustering results. Meanwhile, we design a fast-update-strategy to update labels efficiently. In addition, we propose an active clustering ensemble framework that combines local and global uncertainties to query the most ambiguous instances for better separation between the clusters. A weighted voting consensus method is introduced for better integration of clustering results. We conducted experiments by comparing our methods with state-of-the-art methods on real-world data sets. Experimental results demonstrate the effectiveness of our methods.

In this paper, we investigate a challenging unsupervised domain adaptation setting --- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.

Deep learning demonstrates its impressive success across various machine learning problems. However, its performance often suffers in the case where the training and test data sets follow different distributions, due to the domain shift. Most current domain adaptation methods minimize the discrepancy between the source and target domains by enforcing the alignment of their marginal distributions without considering the class-level matching. Consequently, data from different classes may become close together after mapping. To address this issue, we propose an unsupervised domain adaptation method by generating image-label pairs in the target domain, in which the model is augmented with the generated target pairs and achieve class-level transfer. Specifically, we integrate generative adversarial networks (GAN) into the model predictor, where the generator fed with labels aims to produce corresponding target domain images with a well-designed semantic loss. Meanwhile, compared to previous methods which focus on discrepancy reduction across domains, i.e., image to image translation, our model focuses on semantic preservation during image generation. Our model is straightforward yet effective for unsupervised domain adaptation problems. Without any labels in the target domain in all the experiments, we demonstrate the validity of our approach by presenting the plausible generated target image-label pairs. In addition, our proposed method achieves the best or comparable performance on multiple unsupervised domain adaptation datasets which include image classification and semantic segmentation.

The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise. To address these issues, we propose a hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm. Specifically, we first develop a density-based undersampling method to select informative samples from the original training data with probability-based data transformation, which enables to obtain multiple subsets following a balanced distribution across classes. Second, we exploit the cost-sensitive classification method to address the incompleteness of information problem via modifying weights of misclassified minority samples rather than the majority ones. Finally, we introduce a multi-objective optimization procedure and utilize connections between samples to self-modify the classification result using an ensemble classifier framework. Extensive comparative experiments conducted on real-world data sets demonstrate that our method outperforms the majority of imbalance and ensemble classification approaches.

For unsupervised domain adaptation, the process of learning domain-invariant representations could be dominated by the labeled source data, such that the specific characteristics of the target domain may be ignored. In order to improve the performance in inferring target labels, we propose a targetspecific network which is capable of learning collaboratively with a domain adaptation network, instead of directly minimizing domain discrepancy. A clustering regularization is also utilized to improve the generalization capability of the target-specific network by forcing target data points to be close to accumulated class centers. As this network learns and specializes to the target domain, its performance in inferring target labels improves, which in turn facilitates the learning process of the adaptation network. Therefore, there is a mutually beneficial relationship between these two networks. We perform extensive experiments on multiple digit and object datasets, and the effectiveness and superiority of the proposed approach is presented and verified on multiple visual adaptation benchmarks, e.g., we improve the state-ofthe-art on the task of MNIST→SVHN from 76.5% to 84.9% without specific augmentation.

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.

Multitask clustering methods are proposed to improve performances of related tasks concurrently, because they explore the relationship among tasks via exploiting the coefficient matrix or the shared feature matrix. However, divergent effects of features in learning this relationship are seldom considered. To further improve performances, we propose a new multitask clustering approach through exploring correlations among tasks, clusters, and features based on effects of features on clusters. First, a Feature-Cluster (FeaCluster) matrix is introduced to capture the similarity and the distinct task-feature information simultaneously for each task. With the FeaCluster matrix, two affinities are calculated to constitute the interdependencies among tasks: the former is the graphical affinity based on feature-task and task-cluster correlations, while the latter is the reconstructive affinity. Here, the feature-task correlation considers effects of features on tasks, and the task-cluster correlation considers the overall effects of features on clusters. The reconstructive affinity is obtained by minimizing the reconstruction error when representing the FeaCluster matrix for a given task with a linear combination of others. The interdependencies among tasks allow transferring asymmetric shared information, exploring significant features and preserving key information when mapping data into the subspace. The experimental results on multiple data sets reveal that the proposed approach outperforms the state-of-the-art clustering methods in terms of accuracy and normal mutual information.

Existing domain adaptation approaches generalize models trained on the labeled source domain data to the unlabeled target domain data by forcing feature distributions of two domains closer. However, these approaches are likely to ignore the semantic information during the feature alignment between source and target domain. In this paper, we propose a new unsupervised domain adaptation framework to learn the cross-domain features and disentangle the semantic information concurrently. Specifically, we firstly combine the task-specific classification and domain adversarial learning to obtain the cross-domain features by mapping the data of both domains with the shared feature extractor. Secondly, we integrate the domain adversarial learning and the within-domain reconstruction to disentangle the semantic information from the domain information. Thirdly, we include a cross-domain transformation to further refine the feature extractor, which in turn improves the performances of the task classifier. We compare our proposed model to previous state-of-the-art methods on domain adaptation digit classification tasks. Experimental results show that our model achieves better performances than the other counterparts, which demonstrates the superiority and effectiveness of our model.