Incomplete Multi-view Clustering Research Articles

SADCL-Net: Sparse-driven Attention with Dual-Consistency Learning Network for Incomplete Multi-view Clustering

SADCL-Net: Sparse-driven Attention with Dual-Consistency Learning Network for Incomplete Multi-view Clustering

Graph refinement and consistency self-supervision for tensorized incomplete multi-view clustering

Graph refinement and consistency self-supervision for tensorized incomplete multi-view clustering

Incomplete multi-view clustering via confidence graph completion based tensor decomposition

In recent years, extensive incomplete multi-view clustering models have been proposed to solve the problem of real-world multi-view data with missing views. However, they still have the following two drawbacks: (1) They ignore the harmful effects of outliers in data and the noise generated during graph recovery. (2) They do not fully explore the high-order relationships among views. To this end, we design a novel graph learning model called confidence graph completion based tensor decomposition (CGCTD) for incomplete multi-view clustering. Specifically, we use the confidence graphs to guide the learning of the complete graphs, which reduces the detrimental effects of outliers and missing samples in the data. Then, we stack the complete graphs of each view into an original tensor to explore high-order relationships and correlations between views. To reduce the negative effects of noise, we decompose the original tensor into an essential tensor and a noise tensor. The essential tensor is introduced to recover the accurate affinity graphs, and the noise tensor aims to model the noise contained in the corrupted graphs. Furthermore, we impose the tensor Schatten p-norm constraint on the essential tensor, which can enhance the low-rank property of the graphs and explore the similarity structure between views. Through extensive experiments on eight benchmark datasets, we demonstrate that the proposed CGCTD outperforms several existing state-of-the-art methods.

Projective Incomplete Multi-View Clustering.

Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering, MVC improves clustering performance by exploiting complementary and consistent information among different views. Such methods are all based on the assumption of complete views, which means that all the views of all the samples exist. It limits the application of MVC, because there are always missing views in practical situations. In recent years, many methods have been proposed to solve the incomplete MVC (IMVC) problem and a kind of popular method is based on matrix factorization (MF). However, such methods generally cannot deal with new samples and do not take into account the imbalance of information between different views. To address these two issues, we propose a new IMVC method, in which a novel and simple graph regularized projective consensus representation learning model is formulated for incomplete multi-view data clustering task. Compared with the existing methods, our method not only can obtain a set of projections to handle new samples but also can explore information of multiple views in a balanced way by learning the consensus representation in a unified low-dimensional subspace. In addition, a graph constraint is imposed on the consensus representation to mine the structural information inside the data. Experimental results on four datasets show that our method successfully accomplishes the IMVC task and obtain the best clustering performance most of the time. Our implementation is available at https://github.com/Dshijie/PIMVC.

Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering.

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.

Data Completion-Guided Unified Graph Learning for Incomplete Multi-View Clustering

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data for performance improvement. Unfortunately, some instances may be with partially available information because of some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVC aims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneity of multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods like BSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without taking advantage of the correlation between samples and information redundancy. To overcome the above issue, we propose one novel IMVC method named data completion-guided unified graph learning (DCUGL), which could complete the data of missing views and fuse multiple learned view-specific similarity matrices into one unified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specific similarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensor with the low-rank constraint, such that sample correlation within and between views can be well explored. Finally, by dividing the original data into observed data and unobserved data, DCUGL can infer and complete the missing data according to the view-specific similarity matrices, and obtain a unified graph, which can be directly used for clustering. To solve the proposed model, we design an iterative algorithm, which is based on the alternating direction method of multipliers framework. The proposed model proves to be superior by benchmarking on six challenging datasets compared with state-of-the-art IMVC methods.

Incomplete Multiview Clustering Based on Consensus Information.

In contrast to traditional single-view clustering methods, multiview clustering (MVC) approaches aim to extract, analyze, and integrate structural information from diverse perspectives, providing a more comprehensive understanding of internal data structures. However, with an increasing number of views, maintaining the integrity of view information becomes challenging, giving rise to incomplete MVC (IMVC) methods. While existing IMVC methods have shown notable performance on many incomplete multiview (IMV) databases, they still grapple with two key shortcomings: 1) they treat the information of each view as a whole, disregarding the differences among samples within each view; and 2) they rely on eigenvalue and eigenvector operations on the view matrix, limiting their scalability for large-scale samples and views. To overcome these limitations, we propose a novel multiview clustering with consistent information (IMVC-CI) of sample points. Our method explores the multiview information set of sample points to extract consensus structural information and subsequently restores unknown information in each view. Importantly, our approach operates independently on individual sample points, eliminating the need for eigenvalue and eigenvector operations on the view information matrix and facilitating parallel computation. This significantly enhances algorithmic efficiency and mitigates challenges associated with dimensionality. Experimental results on various public datasets demonstrate that our algorithm outperforms state-of-the-art IMVC methods in terms of clustering performance and computational efficiency. The code for our article has been uploaded to https://github.com/PhdJiayiTang/IMVC-CI.git.

Tensor-based global block-diagonal structure radiation for incomplete multiview clustering

Incomplete multiview clustering (IMVC) has attracted extensive attention in the field of machine learning due to its excellent performance in handling incomplete multiview data. However, existing IMVC methods have certain limitions:1) the completion methods are not flexible enough for cases where view information is arbitrarily missing; 2) the existing methods cannot fully exploit the information contained in the clustering indicator matrix, which is crucial to the clustering results. To solve these problems, we propose a novel method, i.e., tensor-based global block-diagonal structure radiation for incomplete multiview clustering (TGBSR). First, we utilize the information from known samples to construct similarity graphs and integrate these matrices into a third-order tensor. Second, to explore the consensus information among the different similarity graphs, we introduce the spectral embedding technique to obtain the clustering indicator matrix, i.e., U. Third, and more importantly, to fully explore the information contained in the clustering indicator matrix, we introduce a novel matrix, i.e., the UUT, which is low-rank and naturally has an excellent block diagonal structure, and then incorporate it as a new view into the aforementioned tensor, which is constrained by the tensor nuclear norm (TNN), to mine the high-order correlations among all the different views. Finally, we combine all these steps into a unified framework and develop an effective iterative optimization procedure to solve it. The experimental results on several well-known datasets demonstrate the effectiveness of our method. The code is publicly available at https://github.com/hhipp-999/TGBSR.

Deep incomplete multi-view clustering via attention-based direct contrastive learning

Incomplete Multi-View Clustering (IMVC), enhanced by contrastive learning, stands out in unsupervised learning for its notable performance. However, it faces challenges: over-reliance on additional projection heads to avoid dimensionality collapse, leading to redundant parameters, and the risk of encoder-derived features, which merge view-specific information, misleading the learning of common semantics due to simultaneous learning and reconstruction in the same feature space. To address these issues, we propose a novel framework for incomplete multi-view contrastive clustering. This framework employs an encoder network with a self-attention mechanism, allowing both reconstruction loss and contrastive loss to act on the learned feature vectors and their sub-vectors, respectively. This approach effectively mitigates the impact of extraneous private information. By leveraging sub-vectors for consistency learning, our model directly refines the latent feature subspace, thus circumventing dimensionality collapse without the dependence on projection heads. Additionally, our method incorporates a cross-view prediction mechanism to recuperate missing information in incomplete datasets. Comprehensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering performance.

Tensorized Incomplete Multi-view Kernel Subspace Clustering

Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are often faced with three challenging issues. First, they mostly lack the ability to recover the nonlinear subspace structures in the multiple kernel spaces. Second, they usually neglect the high-order relationship in multiple representations. Third, they often have two or even more hyper-parameters and may not be practical for some real-world applications. To tackle these issues, we present a Tensorized Incomplete Multi-view Kernel Subspace Clustering (TIMKSC) approach. Specifically, by incorporating the kernel learning technique into an incomplete subspace clustering framework, our approach can robustly explore the latent subspace structure hidden in multiple views. Furthermore, we impute the incomplete kernel matrices and learn the low-rank tensor representations in a mutual enhancement manner. Notably, our approach can discover the underlying relationship among the observed and missing samples while capturing the high-order correlation to assist subspace clustering. To solve the proposed optimization model, we design a three-step algorithm to efficiently minimize the unified objective function, which only involves one hyper-parameter that requires tuning. Experiments on various benchmark datasets demonstrate the superiority of our approach. The source code and datasets are available at: https://www.researchgate.net/publication/381828300_TIMKSC_20240629.

Low-Rank Tensor Regularized Views Recovery for Incomplete Multiview Clustering.

In real applications, it is often that the collected multiview data contain missing views. Most existing incomplete multiview clustering (IMVC) methods cannot fully utilize the underlying information of missing data or sufficiently explore the consistent and complementary characteristics. In this article, we propose a novel Low-rAnk Tensor regularized viEws Recovery (LATER) method for IMVC, which jointly reconstructs and utilizes the missing views and learns multilevel graphs for comprehensive similarity discovery in a unified model. The missing views are recovered from a common latent representation, and the recovered views conversely improve the learning of shared patterns. Based on the shared subspace representations and recovered complete multiview data, the multilevel graphs are learned by self-representation to fully exploit the consistent and complementary information among views. Besides, a tensor nuclear norm regularizer is introduced to pursue the global low-rank property and explore the interview correlations. An alternating direction minimization algorithm is presented to optimize the proposed model. Moreover, a new initialization method is proposed to promote the effectiveness of our method for latent representation learning and missing data recovery. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches.

Comprehensive consensus representation learning for incomplete multiview subspace clustering

Incomplete multiview clustering (IMVC) aims to address clustering problems caused by missing views. While some progress has been made, the current approaches still have some problems. First, the existing IMVC methods excessively rely on the original high-dimensional multiview data, leading to suboptimal results when the data are heavily contaminated. Second, the utilization of high-dimensional data often overlooks the complementarity of and consistency across multiple views. Third, the cluster structure of the input data is not adequately considered, which is detrimental to the resulting clustering performance. To address these problems, we propose a novel method: comprehensive consensus representation learning for incomplete multiview subspace clustering (CLR-IMVC). Specifically, CLR-IMVC assumes that all views are generated from a latent representation space. We model the original incomplete multiple views and the latent space to explore their correlations. Subsequently, we use the latent space to generate complete views and construct a subspace self-representation matrix for each view. Moreover, we innovatively treat the latent representation itself as new a view and likewise extract its self-representation matrix. Furthermore, we construct a third-order tensor by using these self-representation matrices and impose low-rank constraints on the tensor to explore the high-order correlations among the matrices. In addition, a consensus representation learning term is added to explore the consistency information between the views and the latent space, ultimately generating a robust clustering structure. Finally, the proposed method can be effectively solved by the augmented Lagrangian multiplier (ALM). Extensive experiments conducted on diverse datasets demonstrate the effectiveness of CLR-IMVC.

Low-Rank Graph Completion-Based Incomplete Multiview Clustering.

In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Second, most of the methods depend on local structure information and ignore the global structure information. Third, most of the methods cannot use both global structure information and potential information across views to adaptively recover the incomplete relationship structure. To address the above issues, we propose a unified optimization framework to learn reasonable affinity relationships, called low-rank graph completion-based IMVC (LRGR_IMVC). 1) Our method introduces adaptive graph embedding to effectively explore the potential relationship among views; 2) we append a low-rank constraint to adequately exploit the global structure information among views; and 3) this method unites related information within views, potential information across views, and global structure information to adaptively recover the incomplete graph structure and obtain complete affinity relationships. Experimental results on several commonly used datasets show that the proposed method achieves better clustering performance significantly than some of the most advanced methods.

Fast Incomplete Multi-View Clustering With View-Independent Anchors.

Multi-view clustering (MVC) methods aim to exploit consistent and complementary information among each view and achieve encouraging performance improvement than single-view counterparts. In practical applications, it is common to obtain instances with partially available information, raising researches of incomplete multi-view clustering (IMC) issues. Recently, several fast IMC methods have been proposed to process the large-scale partial data. Though with considerable acceleration, these methods seek view-shared anchors and ignore specific information among single views. To tackle the above issue, we propose a fast IMC with view-independent anchors (FIMVC-VIA) method in this article. Specifically, we learn individual anchors based on the diversity of distribution among each incomplete view and construct a unified anchor graph following the principle of consistent clustering structure. By constructing an anchor graph instead of pairwise full graph, the time and space complexities of our proposed FIMVC-VIA are proven to be linearly related to the number of samples, which can efficiently solve the large-scale task. The experiment performed on benchmarks with different missing rate illustrates the improvement in complexity and effectiveness of our method compared with other IMC methods. Our code is publicly available at https://github.com/Tracesource/ FIMVC-VIA.

Incomplete Data Meets Uncoupled Case: A Challenging Task of Multiview Clustering.

Incomplete multiview clustering (IMC) methods have achieved remarkable progress by exploring the complementary information and consensus representation of incomplete multiview data. However, to our best knowledge, none of the existing methods attempts to handle the uncoupled and incomplete data simultaneously, which affects their generalization ability in real-world scenarios. For uncoupled incomplete data, the unclear and partial cross-view correlation introduces the difficulty to explore the complementary information between views, which results in the unpromising clustering performance for the existing multiview clustering methods. Besides, the presence of hyperparameters limits their applications. To fill these gaps, a novel uncoupled IMC (UIMC) method is proposed in this article. Specifically, UIMC develops a joint framework for feature inferring and recoupling. The high-order correlations of all views are explored by performing a tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on recoupled and inferred self-representation matrices. Moreover, all hyperparameters of the UIMC method are updated in an exploratory manner. Extensive experiments on six widely used real-world datasets have confirmed the superiority of the proposed method in handling the uncoupled incomplete multiview data compared with the state-of-the-art methods.

Robust tensor ring-based graph completion for incomplete multi-view clustering

Incomplete multi-view clustering (IMVC) aims to enhance clustering performance by leveraging complementary information from multi-view data, even in the presence of missing instances. This is challenging due to the interference caused by these missing data points. Current IMVC algorithms generally adopt two main strategies: either disregarding the missing instances and focusing on observable data for clustering, or complementing the missing instances within each view to improve the clustering results. However, effectively leveraging the latent information within these missing instances remains a challenge. In response, we propose a novel IMVC framework that complements the tensor consisting of similarity matrices learned from the available instances to enhance the relationships among samples from different views. In addition, we integrate tensor ring completion and M-estimator-based methods into the IMVC approach. This promotes the integration of inter-view information and mitigates errors introduced by missing instances in the model, respectively. Furthermore, we develop two efficient half-quadratic (HQ) based iterative algorithms with soft/hard threshold or multivariate generalization of minimax-concave (GMC) penalty to regularize the low-rank property. Comprehensive evaluations on seven benchmark datasets demonstrate that our method outperforms state-of-the-art IMVC approaches across various metrics.

Low-rank representation induced missing-view recovery for incomplete multi-view clustering

In practical applications, multi-view data suffers from incompleteness, which refers to the absence of some samples in each view. Incomplete multi-view clustering is developed to capture the common cluster structure across incomplete views. However, some existing IMC methods ignore the missing samples or the latent relationship between the available and the unavailable samples. We propose a novel missing-view recovery strategy to overcome these two drawbacks. Considering that each missing sample and part of the observable samples belong to the same category, we recover each missing sample via the linear combination of observed samples within each view. Based on the semantic consistency, we extract a unified self-representation graph from complete views and impose a low-rank constraint on it. Furthermore, we learn a comprehensive representation from complete multi-view data and introduce the dynamic weights to consider the importance of various views. The experimental results on several datasets demonstrate that the proposed method outperforms the state-of-the-art IMC approaches.

View-interactive attention information alignment-guided fusion for incomplete multi-view clustering

Multi-view data from real applications is usually incomplete, leading to unsatisfactory performance in clustering tasks. Existing incomplete multi-view clustering (IMVC) methods still encounter the following issues: (1) They focus on learning consistency features between different views but often struggle to recover missing data based on the complementary features between views. (2) Differing from complete multi-view data, the misaligned characteristics of incomplete multi-view data may introduce noises in feature learning, thereby affecting the performance of the IMVC models. To address these issues, we propose a novel view-interactive attention information alignment-guided fusion (VAIAF) method for clustering incomplete multi-view data. Firstly, it utilizes an autoencoder to extract feature information from each view. Then we construct an information interactive attention (IIA) module to learn multi-view information by introducing the self-attention mechanism. This module not only extracts consistent features from different views but also utilizes the feature complementary relationships between views to recover missing data to a certain extent. Therefore, it can enhance the capability of the model to learn the consistency and complementarity of incomplete multi-view data. The proposed VAIAF method also constructs a discriminator for each view to facilitate missing data recovery. In the feature fusion stage, we implement an alignment-guided strategy to prevent noise caused by misaligned feature fusion. Extensive experiments on four incomplete multi-view datasets demonstrate the effectiveness of the proposed VAIAF method in clustering tasks.

Deep embedding based tensor incomplete multi-view clustering

The majority of multi-view data are extracted from real life, which often lose information in some views. To solve this problem, existing incomplete multi-view clustering algorithms explore the valuable information from incomplete data while populating the missing information. Nevertheless, they suffer from the following two limitations: 1) The non-linear relationships of high-dimensional available data are not considered. 2) Although some methods utilize information from different views to fill in missing information, they still cannot precisely explore the missing information of each view. To this end, this article proposes a novel one-step incomplete multi-view framework, referred to as deep embedding based tensor incomplete multi-view clustering (DETIMC). Concretely, in this framework, the high-dimensional available data are projected into the low-dimensional embedding space by deep non-negative matrix factorization (NMF), which can obtain a clean space while capturing the complex non-linear relationship of available data. Moreover, a novel double-enhanced missing-view inferring strategy is developed, in which the weighted higher-order information of different views and the clustering structure information are simultaneously exploited. Comprehensive experiments on several benchmark datasets exhibit the superiority of DETIMC over conventional and state-of-the-art algorithms.

Graph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-View Clustering

Graph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-View Clustering