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Robust deep multi-view subspace clustering networks with a correntropy-induced metric

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Abstract

Since multi-view subspace clustering combines the advantages of deep learning to capture the nonlinear nature of data, deep multi-view subspace clustering methods have demonstrated superior ability to shallow multi-view subspace clustering methods. Most existing methods assume that sample reconstruction errors incurred by noise conform to the prior distribution of the corresponding norm, allowing for simplification of the problem and focus on designing specific regularization on self-representation matrices to exploit consistent and diverse information among different views. However, the noise distributions in different views are always very complex, and in practice the noise distributions do not necessarily conform to this hypothesis. Furthermore, the commonly used diversity regularization based on value-awareness to enhance diversity among different view representations is not sufficiently accurate. To alleviate the above deficiencies, we propose novel robust deep multi-view subspace clustering networks with a correntropy-induced metric (RDMSCNet). (1) A correntropy-induced metric (CIM) is utilized to flexibly handle various complex noise distributions in a data-driven manner to improve the robustness of the model. (2) A position-aware diversity regularization based on the exclusivity definition is employed to enforce the diversity of the different view representations for modelling the consistency and diversity simultaneously. Extensive experiments show that RDMSCNet can deliver enhanced performance over state-of-the-art approaches.

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Notes

  1. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

  2. http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html.

  3. https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

  4. http://mlg.ucd.ie/datasets/segment.html.

  5. http://www.vision.caltech.edu/ImageDatasets/Caltech101/

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Acknowledgements

This study was funded by the Key Program of National Natural Science Foundation of China (grant no. 61731003) and the Funds for National Natural Science Foundation of China (grant no. 61871040).

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Haidian, Beijing, China

    Xiaomeng Si, Xiaojie Zhao & Li Yao

  2. Institute of Automation, Chinese Academy of Sciences, Haidian, Beijing, China

    Qiyue Yin

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  1. Xiaomeng Si
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  2. Qiyue Yin
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  3. Xiaojie Zhao
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  4. Li Yao
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Correspondence to Li Yao.

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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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Si, X., Yin, Q., Zhao, X. et al. Robust deep multi-view subspace clustering networks with a correntropy-induced metric. Appl Intell 52, 14871–14887 (2022). https://doi.org/10.1007/s10489-022-03209-9

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