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

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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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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61906030), the Youth Science and Technology Star Support Program of Dalian, 2021RQ057, the Natural Science Foundation of Liaoning Province (2020-BS-063) and the Equipment Advance Research Fund (80904010301).

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  1. School of Software Technology, Dalian University of Technology, Dalian, Liaoning, China

    Liang Zhao, Jie Zhang, Tao Yang & Zhikui Chen

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  1. Liang Zhao
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  2. Jie Zhang
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Correspondence to Liang Zhao.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Zhao, L., Zhang, J., Yang, T. et al. Incomplete multi-view clustering based on weighted sparse and low rank representation. Appl Intell 52, 14822–14838 (2022). https://doi.org/10.1007/s10489-022-03246-4

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