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SADCL-Net: Sparse-driven Attention with Dual-Consistency Learning Network for incomplete multi-view clustering

multiMEDIA SYSTEMS 2024年第5期30卷 1-19页

作者： Xue, Sicheng Zhu, Changming Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

In recent years, various deep learning-based methods have been proposed to address the problem of incomplete multi-view clustering. However, these methods still face two major challenges: firstly, due to the incompleteness of views, they are susceptible to noise and irrelevant features, leading to redundant and untargeted learned features;secondly, although these methods attempt to leverage the consistent information across different views for cross-view learning, they often overemphasize consistency, thereby neglecting inter-view differences. To address these issues, this paper proposes a novel method: Sparse-driven Attention with Dual-Consistency Learning Network for incomplete multi-view clustering (SADCL-Net). Specifically, we utilize a sparsity-constrained self-attention module to effectively capture the intrinsic sparsity and local salient information of the data, thereby highlighting key features. Subsequently, we design a Dual-Consistency Learning module, which incorporates a joint entropy term into the consistency loss function to balance the consistency and difference information across views, thereby optimizing the contribution of different views to the final clustering results. Additionally, to avoid potential issues arising from imputing missing data, we integrate feature projection and alignment modules. Our proposed method has been extensively evaluated on six widely-used multi-view datasets, with results prominently demonstrating the significant advantages of SADCL-Net in terms of clustering performance.

关键词： incomplete multi-view clustering Sparsity-constrained self-attention Dual-consistency learning Feature projection and alignment

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Tensor-Based incomplete multi-view clustering With Low-Rank Data Reconstruction and Consistency Guidance

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第12期33卷 7156-7169页

作者： Hao, Wenyu Pang, Shanmin Bai, Xiuxiu Xue, Jianru Xi An Jiao Tong Univ Sch ofSoftware Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Visual Cognit Comp & Intelligent Vehicle Lab VCC & Xian Peoples R China

We propose a new approach, called Tensor-based incomplete multi-view clustering with Low-rank data Reconstruction and Consistency guidance (TIMC-RC), to perform clustering on multi-view data with missing views. Existing methods usually leverage original incomplete data to explore the partial correlations among multiple views, and do not make sufficient use of both consistent and complementary information across views. To explore the full information of missing and available views, TIMC-RC introduces low-rank data reconstruction and consistency view establishment. Specifically, 1) it adopts a low-rank constraint to reconstruct data representations so as to reduce the negative effect of missing data and obtain more reasonable data representations. 2) It builds a new consistency view by self-representation matrices and therefore explores the consistent correlation of different views. 3) It formalizes view-specific self-representation matrices and the consistent matrix as a tensor and utilizes the tensor singular value decomposition-based nuclear norm to enhance the consistency and complementarity of multi-view representations. Experiments conducted on eight benchmarks verify the effectiveness and advancement of the proposed TIMC-RC.

关键词： incomplete multi-view clustering t-SVD low-rank

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incomplete multi-view clustering by simultaneously learning robust representations and optimal graph structures

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INFORMATION SCIENCES 2023年第1期640卷

作者： Shang, Mingchao Liang, Cheng Luo, Jiawei Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

incomplete multi-view clustering aims to assign data samples into cohesive groups with partially available information from multiple views. In this paper, we propose a novel generalized incomplete multi-view clustering method that integrates latent representation learning, spectral embedding as well as optimal graph clustering into a unified framework. Specifically, our method first learns a set of view-specific latent representations via low-rank subspace recovery. To bridge the gap brought by the missing samples from each view, we then unify the dimensions of the latent representations and obtain the spectral embeddings by preserving the local geometric structures. Moreover, our model seeks a consensus similarity matrix with optimal cluster structures by simultaneously approximating the affinity graphs reconstructed from multiple views. As a result, both intrinsic subspace structures and the global cluster structure could be mutually optimized by exploiting the consistent information shared among view-specific available samples. Compared with seven competing techniques, the experimental results confirm the superiority of our method on two different types of incomplete multi-view clustering tasks.

关键词： incomplete multi-view clustering Low-rank representation Spectral embedding Graph clustering

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

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Cheng, Yuanbo Song, Peng Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

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.

关键词： incomplete multi-view clustering Graph learning Confidence graph Tensor decomposition Tensor Schatten p-norm

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Nonnegative Tensor Representation With Cross-view Consensus for incomplete multi-view clustering

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2605-2609页

作者： Zhong, Guo Wu, Juanchun Yan, Xueming Ma, Xuanlong Lin, Shixun Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China Xian Shiyou Univ Sch Sci Xian 710065 Peoples R China Zhaotong Univ Sch Math & Stat Zhaotong 657000 Peoples R China

Tensors capture the multi-dimensional structure of multi-view data naturally, resulting in richer and more meaningful data representations. This produces more accurate clustering results for challenging incomplete multi-view clustering (IMVC) tasks. However, previous tensor learning-based IMVC (TLIMVC) methods often build a tensor representation by simply stacking view-specific representations. Consequently, the learned tensor representation lacks good interpretability since each entry of it could not directly reveals the similarity relationship of the corresponding two samples. In addition, most of them only focus on exploring the high-order correlations among views, while the underlying consensus information is not fully exploited. To this end, we propose a novel TLIMVC method named Nonnegative Tensor Representation with Cross-view Consensus (NTRC$<^>clustering$) in this paper. Specifically, a nonnegative constraint and view-specific consensus are jointly integrated into the framework of the tensor based self-representation learning, which enables the method to simultaneously explore the consensus and complementary information of multi-view data more fully. An Augmented Lagrangian multiplier based optimization algorithm is derived to optimize the objective function. Experiments on several challenging benchmark datasets verify our NTRC$<^>clustering$ method's effectiveness and competitiveness against state-of-the-art methods.

关键词： Data reconstruction incomplete multi-view clustering low-rank tensor representation Data reconstruction incomplete multi-view clustering low-rank tensor representation

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Adaptive Feature Projection With Distribution Alignment for Deep incomplete multi-view clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 1354-1366页

作者： Xu, Jie Li, Chao Peng, Liang Ren, Yazhou Shi, Xiaoshuang Shen, Heng Tao Zhu, Xiaofeng Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518000 Peoples R China Guangxi Acad Sci Nanning 530007 Peoples R China

incomplete multi-view clustering (IMVC) analysis, where some views of multi-view data usually have missing data, has attracted increasing attention. However, existing IMVC methods still have two issues: 1) they pay much attention to imputing or recovering the missing data, without considering the fact that the imputed values might be inaccurate due to the unknown label information, 2) the common features of multiple views are always learned from the complete data, while ignoring the feature distribution discrepancy between the complete and incomplete data. To address these issues, we propose an imputation-free deep IMVC method and consider distribution alignment in feature learning. Concretely, the proposed method learns the features for each view by autoencoders and utilizes an adaptive feature projection to avoid the imputation for missing data. All available data are projected into a common feature space, where the common cluster information is explored by maximizing mutual information and the distribution alignment is achieved by minimizing mean discrepancy. Additionally, we design a new mean discrepancy loss for incomplete multi-view learning and make it applicable in mini-batch optimization. Extensive experiments demonstrate that our method achieves the comparable or superior performance compared with state-of-the-art methods.

关键词： incomplete multi-view clustering adaptive fea-ture projection distribution alignment deep feature learning

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Graph Regularized and Feature Aware Matrix Factorization for Robust incomplete multi-view clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3728-3741页

作者： Wen, Jie Xu, Gehui Tang, Zhanyan Wang, Wei Fei, Lunke Xu, Yong Harbin Inst Technol Shenzhen Key Lab Visual Object Detect & Recognit Shenzhen 518055 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

In recent years, many incomplete multi-view clustering methods have been proposed to address the challenging and new clustering task on incomplete multi-view data whose part of view representations are not fully collected for some samples. Although extensive experiments have validated the effectiveness of these methods for handling the incomplete learning issue, a common issue exists, i.e., these methods all ignore the discriminative/important difference of discriminative features and noisy features. In this paper, to address the above issue, a new incomplete multi-view clustering model, called Graph Regularized and fEature Aware maTrix Factorization (GreatF), is proposed. Different from the existing methods, we introduce an adaptive feature weighting constraint to the matrix factorization-based multi-view representation learning model. With this weighting constraint, the effect of the discriminative features can be enhanced while the negative effect caused by the redundant and noisy features can be eliminated for the model optimization;thus, the robustness of the model can be enhanced. In addition, in this work, we designed a new graph-embedded consensus representation learning term in which consensus representation learning and structure information preservation are integrated into a joint model with one term. In particular, this term provides a more concise approach to obtain the structured consensus representation from incomplete multi-view data. Experimental results on four well-known datasets demonstrate that GreatF performs better than the state-of-the-art incomplete multi-view clustering methods.

关键词： incomplete multi-view clustering graph regularization structured consensus representation multi-view learning

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Structured anchor-inferred graph learning for universal incomplete multi-view clustering

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2023年第1期26卷 375-399页

作者： He, Wenjue Zhang, Zheng Chen, Yongyong Wen, Jie Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China

The goal of multi-view spectral clustering (MVSC) is to explore the intrinsic cluster structures embedded in the multi-view data and group the learned optimal feature embeddings into different clusters based on similarity measurement. Although encouraging improvements have been achieved, when facing the incomplete multi-view data, these MVSC methods would be disabled since most of them are generally built on the common assumption that data is required to have complete multiple descriptions, which obviously violates real-world situations. In this paper, we propose a novel Structured Anchor-inferred Graph Learning (SAGL) method to tackle the challenging universal incomplete multi-view spectral clustering problem, which can handle arbitrary view missing cases. Specifically, instead of using the fixed distance-based weighting matrix in the existing incomplete MVSC, we construct a structural anchor-based similarity learning model to formulate a learnable asymmetric intra-view similarity matrix. Meanwhile, the inter-view similarities can be successfully bridged by the paired anchor samples, which can skillfully overcome the limitation of insufficient information operations on incomplete multi-view data. Particularly, we further extend the two-view cases to the late fusion version of universal cases for accurate similarity calculation on incomplete multi-view data. Moreover, we derive a complete anchor-inferred graph learning scheme to enhance the efficiency of the spectral clustering process, which can well capture the hidden connection information among multi-view data, yielding improved clustering performance. Furthermore, we design a fast learning algorithm to solve the resulting optimization problem. Extensive experiments on multiple multi-view datasets show the superiority and advantages of the proposed method when handling different types of multi-view data with arbitrary information missing.

关键词： incomplete multi-view clustering Spectral clustering multi-view data Similarity measurement

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Data Completion-Guided Unified Graph Learning for incomplete multi-view clustering

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2024年第8期18卷 1-23页

作者： Liang, Tianhai Shen, Qiangqiang Wang, Shuqin Chen, Yongyong Zhang, Guokai Chen, Junxin Harbin Inst Technol Sch Mech Engn & Automat Shenzhen Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen Peoples R China Shandong Univ Aeronaut Coll Sci Binzhou Peoples R China Harbin Inst Technol Biocomp Res Ctr Shenzhen Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data forperformance improvement. Unfortunately, some instances may be with partially available information becauseof some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVCaims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneityof multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods likeBSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without takingadvantage 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), whichcould complete the data of missing views and fuse multiple learned view-specific similarity matrices into oneunified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specificsimilarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensorwith the low-rank constraint, such that sample correlation within and between views can be well ***, by dividing the original data into observed data and unobserved data, DCUGL can infer and completethe 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 basedon the alternating direction method of multipliers framework. The proposed model proves to be superior bybenchmarking on six challenging datasets compared with state-of-the-art IMVC methods.

关键词： incomplete multi-view clustering tensor completion low-rank tensor learning

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Feature Space Recovery for Efficient incomplete multi-view clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4664-4677页

作者： Long, Zhen Zhu, Ce Comon, Pierre Ren, Yazhou Liu, Yipeng Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Grenoble Alpes GIPSA Lab CNRS Grenoble INP F-38000 Grenoble France

The tensor singular value decomposition (t-SVD) based incomplete multi-view clustering (IMVC) has received wide attention due to its ability to capture high-order correlations. However, t-SVD suffers from rotation sensitivity, failing to fully explore both inter- and intra-view consistencies. Besides, current methods mainly consider inter- or intra-view correlations, ignoring the low-rank information of sample features within views. To address these weaknesses, we first propose a feature space recovery based IMVC (FSR-IMVC) method, where low-rank feature space recovery and low-rank tensor ring based consistency learning are considered into a unified framework. Furthermore, we extend FSR-IMVC by incorporating anchor learning on the latent feature space, resulting in a scalable FSR-IMVC (sFSR-IMVC) approach that is well-suited to large-scale data. In an iterative way, the learned inter- and intra-view correlations will guide the recovery of missing features, while the explored low-rank information from feature spaces will in turn facilitate consistency exploration, eventually achieving outstanding clustering performance. Experimental results show that FSR-IMVC provides a significant improvement over known state-of-the-art algorithms in terms of ACC, NMI and Purity. Compared with FSR-IMVC, sFSR-IMVC performs slightly worse in clustering accuracy, but offers a notable advantage in computational efficiency, particularly for large-scale datasets.

关键词： Tensors Correlation Task analysis clustering algorithms Space exploration Social networking (online) Feature extraction Anchor learning feature space recovery incomplete multi-view clustering low-rank tensor ring approximation

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