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incomplete multi-view clustering based on weighted sparse and low rank representation

APPLIED INTELLIGENCE 2022年第13期52卷 14822-14838页

作者： Zhao, Liang Zhang, Jie Yang, Tao Chen, Zhikui Dalian Univ Technol Sch Software Technol Dalian Liaoning Peoples R China

multi-view clustering utilizes the consistency and complementarity between views to group entities well. However, in real life, the lack of instances in some views often occurs, which not only reduces the available information, but also increases the difficulty of joint learning with non-aligned multi-view data. Many incomplete multi-view clustering algorithms are developed to tackle these concerns, but they usually have the following problems: 1) They mainly focus on how to construct the shared feature space for incomplete views while ignoring the essential relationship between data instances. 2) Most of them simply assume that two datapoints which are close belong to the same category, but that is not the case. 3) The hazards of overlapping, confusing features in incomplete multi-view clustering are not considered. To solve these issues, this paper proposes a new incomplete multi-view Graph Learning method based on Weighted Sparse and Low rank Representation (IMGLWSLR). It leverages subspace learning with double constraints to capture global and local data relationships, a weighting mechanism to reduce the negative impact of missing data and a kernel-based method to fuse incomplete multiple views. Different from previous approaches, we concentrate on inhibiting the confusion of redundant features in subspace learning, which may affect the clustering seriously with missing views. Experimental results demonstrate the superiority of IMGLWSLR over nine benchmark datasets, compared with seven state-of-the-art approaches.

关键词： incomplete multi-view clustering Graph learning Sparse and low rank representation Weighting mechanism

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incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization

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APPLIED INTELLIGENCE 2022年第13期52卷 14607-14623页

作者： Liang, Naiyao Yang, Zuyuan Li, Zhenni Han, Wei Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab iDetect & Mfg IoT Guangzhou 510006 Peoples R China Guangzhou Railway Polytech Sch Elect Engn Guangzhou 510430 Guangdong Peoples R China

incomplete multi-view clustering (IMC) has achieved widespread attention due to its advantage in fusing the multi-view information when the view samples are unobserved partly. Recently, it is shown that the clustering performance in the subspace can be improved by preserving the clustering structure of each view, but the problem of the inconsistent clustering structure caused by the incomplete graphs are seldom considered, restricting the clustering performance. Motivated by the clustering interpretation of the orthogonal non-negative matrix factorization, it is employed to unify the clustering structure of the data, and a new model called incomplete Graph-regularized Orthogonal Non-negative Matrix Factorization (IGONMF) is proposed in this paper. In IGONMF, the reproduced representation is developed, based on which, a set of incomplete graphs are utilized to fully take advantage of the geometric structure of the data. And the orthogonality is further employed to alleviate the problem of the inconsistent clustering structure. Also, we design an effective iterative updating algorithm to solve the proposed model, along with its analysis on the convergence and the computational cost. Finally, experimental results on several real-world datasets indicate that our method is superior to the related state-of-the-art methods.

关键词： clustering incomplete multi-view clustering Orthogonal non-negative matrix factorization Graph constraint

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incomplete multi-view clustering Based on Dynamic Dimensionality Reduction Weighted Graph Learning

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IEEE ACCESS 2024年 12卷 19087-19099页

作者： Yu, Yaosong Sun, Dongpu Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China

Aiming at the existing incomplete multi-view clustering methods that usually ignore the noise and redundancy of the original data, hide the valuable information in the missing views, and the different importance of each view, this paper proposes the incomplete multi-view clustering based on dynamic dimensionality reduction weighted graph learning (ARDGL), which is mainly divided into two parts: learning the similarity matrix by dynamic dimensionality reduction weighted graph and fusion of the self-weighted graph. In the process of learning the similarity matrix, the noise and redundancy of the original data are effectively filtered by the dynamic dimensionality reduction weighted graph learning, and the influence of incomplete data on the clustering results is attenuated. In the process of self-weighted graph fusion, multiple views are valued as having different importance by introducing view weights, and non-negative orthogonality constraints are added to improve the quality of the consensus matrix, so that clustering results can be obtained without post-processing. To solve the objective function, an alternating iteration algorithm is proposed. Experiments are conducted on four datasets and the results show that the algorithm has better performance.

关键词： multi-view clustering incomplete multi-view clustering graph learning matrix decomposition non-negative orthogonal constraints

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incomplete multi-view clustering With Sample-Level Auto-Weighted Graph Fusion

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第6期35卷 6504-6511页

作者： Liang, Naiyao Yang, Zuyuan Xie, Shengli Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Guangdong Peoples R China

incomplete multi-view clustering (IMC) has received considerable attention due to its flexibility in fusing the multi-view information when the view samples are partly missing. However, existing methods seldom consider the affection of the missing samples to the contributions of the views. In this paper, we propose a novel graph fusion based IMC model (SAGF_IMC) to handle this problem. Instead of directly weighting the whole view, SAGF_IMC learns the sample-level auto weight, which allows considering both the contributions of different views and the affection of the missing samples. An effective iterative algorithm is developed, together with its convergence analysis. Experiments are provided to demonstrate that SAGF_IMC is superior to the related state-of-the-art methods by using several real-world datasets.

关键词： Convergence Fuses Computational modeling Analytical models Optimization Laplace equations Indexes incomplete multi-view clustering graph fusion sample weight auto-weight learning

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incomplete multi-view clustering via self-attention networks and feature reconstruction

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APPLIED INTELLIGENCE 2024年第4期54卷 2998-3016页

作者： Zhang, Yong Jiang, Li Liu, Da Liu, Wenzhe Huzhou Univ Coll Informat Engn Huzhou 313000 Peoples R China Liaoning Normal Univ Sch Comp Sci & Informat Technol Dalian 116029 Liaoning Peoples R China

Over the past few years, numerous deep learning-based methods have been proposed for incomplete multi-view clustering. However, these approaches overlook two crucial issues. First, they focus solely on the global information contained in the latent representations derived from deep networks, neglecting the importance of local focal points. Second, while leveraging consistent or complementary inter-view information for cross-view learning, they disregard the intrinsic relationships among different samples within the same view. To address these concerns, this manuscript presents an original approach: incomplete multi-view clustering based on self-attention networks and feature reconstruction (SNFR). Specifically, SNFR initially employs self-attention networks to emphasize the pivotal information within views, aiming to reduce the inter-view reconstruction loss. Subsequently, an improved entropy weighting method is applied to reconstruct the feature relationships among the diverse samples within the same view, thereby facilitating consistent cross-view information learning. Our proposed method is evaluated on six widely used multi-view datasets through extensive experiments, highlighting its remarkable superiority over the alternative approaches in terms of clustering performance

关键词： incomplete multi-view clustering Self-attention Enhanced entropy weighting method

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incomplete multi-view clustering With Complete view Guidance

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 1247-1251页

作者： Chen, Zhikui Li, Yue Lou, Kai Zhao, Liang Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China Dalian Univ Technol Serv Software Liaoning Prov Key Lab Ubiquitous Network Dalian 116620 Peoples R China

In recent years, multi-view clustering has gained widespread attention in signal processing because multi-view data contains more information than a single view. However, multi-view data is often incomplete due to missing data in one or more random views. Therefore, several methods have been proposed for incomplete multi-view clustering to learn features that contain consensus information for clustering incomplete multi-view data (IMD). However, there is a part of the IMD that is not missing in any view, and most previous methods have not utilized this part to guide the process of learning consensus information. To address this issue, we design a knowledge distillation framework for incomplete multi-view clustering and propose an incomplete multi-view clustering with complete view guidance (IMC-CVG). We first train a robust teacher model with contrastive learning loss on the complete part of IMD to learn consensus features containing multi-view information. Then, we train a student model on all the IMD, where we mask partial views of the complete data to simulate missing data, and utilize the teacher model to guide the student model to learn consensus features that contain as much multi-view information as possible. Experiments show that our proposed method outperforms all the compared state-of-the-art methods.

关键词： Data models Mathematical models Transformers Brain modeling Training Software Signal processing incomplete multi-view clustering distillation learning contrastive learning

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incomplete multi-view clustering via local and global bagging of anchor graphs

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Li, Ao Xu, Haoyue Feng, Cong Yang, Hailu Xu, Shibiao Harbin Univ Sci & Technol Sch Comp Sci & Technol 52Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Beijing Univ Posts & Telecommun Artificial Intelligence Sch Beijing 100876 Peoples R China

Anchor graphs are powerful tools for large-scale applications, but their quality depends on the selection of representative anchors, which is a difficult endeavor. Another challenge is to cluster data with incomplete multi -view features, which have missing values in some views. This paper proposes a novel method for incomplete multi -view clustering based on local and global bagging of anchor graphs. Our method does not require a specific anchor selection scheme but leverages bagging techniques to reduce the variance and discover the intrinsic anchor graph. Specifically, we first construct bagged partial anchor graphs for each incomplete view by randomly sampled anchors. Then, we integrate the bagged partial anchor graphs from multiple views into a unified and complete anchor graph. Finally, we apply global bagging to the unified anchor graph to further boost performance. Our method can generate robust anchor graphs for incomplete multi -view clustering scenarios, as evidenced by extensive experiments.

关键词： incomplete multi-view clustering Anchor graph Bagging Ensemble learning Spectral clustering

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incomplete multi-view clustering network via nonlinear manifold embedding and probability-induced loss

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NEURAL NETWORKS 2023年 163卷 233-243页

作者： Huang, Cheng Cui, Jinrong Fu, Yulu Huang, Dong Zhao, Min Li, Lusi South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Guangzhou Key Lab Intelligent Agr Guangzhou 510642 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

incomplete multi-view clustering, which included missing data in different views, is more challenging than multi-view clustering. For the purpose of eliminating the negative influence of incomplete data, researchers have proposed a series of solutions. However, the present incomplete multi-view clustering methods still confront three major issues: (1) The interference of redundant features hinders these methods to learn the most discriminative features. (2) The importance role of local structure is not considered during clustering. (3) These methods fail to utilize data distribution information to guide models update to decrease the effects of outliers and noise. To address above issues, a novel deep clustering network which exerted on incomplete multi-view data was proposed in this paper. We combine multi-view autoencoders with nonlinear manifold embedding method UMAP to extract latent consistent features of incomplete multi-view data. In the clustering method, we introduce Gaussian Mixture Model (GMM) to fit the complex distribution of data and deal with the interference of outliers. In addition, we reasonably utilize the probability distribution information generated by GMM, using probability-induced loss function to integrate feature learning and clustering as a joint framework. In experiments conducted on multiple benchmark datasets, our method captures incomplete multi-view data features effectively and perform excellent. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： incomplete multi-view clustering Deep clustering Consistent learning Manifold learning Gaussian mixture models

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incomplete multi-view clustering VIA INFERENCE AND EVALUATION 49

INCOMPLETE MULTI-VIEW CLUSTERING VIA INFERENCE AND EVALUATIO...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Binqiang Huang, Zhijie Lan, Shoujie Zheng, Qinghai Yu, Yuanlong Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

multi-view clustering aims to improve the clustering performance by leveraging information from multiple views. Most existing works assume that all views are complete. However, samples in real-world scenarios cannot be always observed in all views, leading to the challenging problem of incomplete multi-view clustering (IMVC). Although some attempts are made recently, they still suffer from the following two limitations: (1) they usually adopt shallow models, which are unable to sufficiently explore the consistency and complementary of multiple views;(2) they lack of a suitable measurement to evaluate the quality of the recovered data during the learning process. To address the aforementioned limitations, we introduce a novel incomplete multi-view clustering via Inference and Evaluation (IMVC-IE). Specifically, IMVC-IE adopts the contrastive learning strategy on features of different views to excavate the underlying information from existing samples firstly. Subsequently, massive alternative simulated data are inferred for missing views and a novel evaluation strategy is presented to obtain the proper data for missing views completion. Extensive experiments are conducted and verify the effectiveness of our method.

关键词： incomplete multi-view clustering missing data inference data evaluation

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Anchor-based sparse subspace incomplete multi-view clustering

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WIRELESS NETWORKS 2024年第6期30卷 5559-5570页

作者： Li, Ao Feng, Cong Wang, Zhuo Sun, Yuegong Wang, Zizhen Sun, Ling Harbin Univ Sci & Technol Sch Comp Sci & Technol 52 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Heilongjiang Univ Ind & Business Sch Informat & Electromech Engn 33 Qunying St Harbin 150025 Heilongjiang Peoples R China

In recent decades, multi-view clustering has received a lot of attention. The majority of previous research has assumed that all instances have complete views or at least one view that includes all instances. However, the incomplete multi-view clustering issue arises because real-world data frequently lack instances in each view. We propose a novel anchor-based sparse subspace incomplete multi-view clustering solution to this issue. Through a unified sparse subspace learning framework, the proposed method learns inter-view anchor-to-anchor and intra-view anchor-to-incomplete affinities and fuses them into a consensus sparse anchor graph, which yields a unified clustering result. Our method outperforms other incomplete multi-view clustering methods in three important ways: (1) it uses a small number of hyperparameters to learn a sparse consensus graph from the data;(2) Because of the anchor-based graph construction, it can process large datasets;(3) It is naturally capable of handling both negative entries and multiple views. Last but not least, extensive experiments show that the proposed method is effective, supporting the claim that it consistently outperforms current clustering methods.

关键词： incomplete multi-view clustering Anchor graph Sparse subspace clustering Spectral clustering

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