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View all- Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
Incomplete multi-view clustering based on weighted sparse and low rank representation
Authors: 
Liang Zhao, Jie Zhang, Tao Yang, Zhikui ChenAuthors Info & Claims
Liang Zhao, Jie Zhang, Tao Yang, Zhikui ChenAuthors Info & ClaimsPages 14822 - 14838
Abstract
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.
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Published: 01 October 2022
Accepted: 14 January 2022
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View all- Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024