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Diversity and consistency embedding learning for multi-view subspace clustering

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Abstract

With the emergence of multi-view data, many multi-view clustering methods have been developed due to the effectiveness of exploiting the complementary information of multi-view data. However, most existing multi-view clustering methods have the following two drawbacks: (1) they usually explore the relationships between samples in the original space, where the high-dimensional features contain noise and outliers; (2) they only pay attention to exploring the consistency or enhancing the diversity of different views, such that the multi-view information cannot be completely utilized. In this paper, we propose a novel multi-view subspace clustering method, namely Diversity and Consistency Embedding Learning (DCEL), which learns a better affinity matrix in a learned latent embedding space while simultaneously considering diversity and consistency of multi-view data. Specifically, by leveraging a projection method, the multi-view data in the latent embedding space can be learned. Then, with the self-expression property, we seek a shared consistent representation among all views and a set of diverse representations of each view to better learn an affinity matrix in the latent embedding space. Furthermore, we develop an optimization scheme based on the alternating direction method of multipliers (ADMM) to solve the proposed method. Experimental evaluations on five benchmark datasets show the superiority of our method, compared with two single-view clustering methods and some state-of-the-art multi-view clustering methods.

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Acknowledgements

This research was supported by the Sichuan Major Science and Technology Project (Grant nos. 2019ZDZX0119 and 2020ZDZX0014), the Scientific Research Fund of SiChuan Provincial Education Department (Grant no. 17ZB0441), the Key Lab of Film and TV Media Technology of Zhejiang Province (Grant no. 2020E10015), and the Major Cultivation Research Projects of Chongqing Three Gorges University (Grant no. 18ZDPY07).

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Authors and Affiliations

  1. The Department of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Yong Mi & Yuqing Huang

  2. The Department of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Zhenwen Ren

  3. Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhenwen Ren

  4. The College of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210094, China

    Mithun Mukherjee

  5. The Department of Computer Science, Nanjing University of Science and Technology, Nanjing, 210094, China

    Quansen Sun

  6. The Department of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing, 404199, China

    Liwan Chen

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  1. Yong Mi
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  2. Zhenwen Ren
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  3. Mithun Mukherjee
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  4. Yuqing Huang
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  5. Quansen Sun
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  6. Liwan Chen
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Correspondence to Zhenwen Ren.

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Mi, Y., Ren, Z., Mukherjee, M. et al. Diversity and consistency embedding learning for multi-view subspace clustering. Appl Intell 51, 6771–6784 (2021). https://doi.org/10.1007/s10489-020-02126-z

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