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Kernelized multi-view subspace clustering via auto-weighted graph learning

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Abstract

Multi-view subspace clustering has been an important and powerful tool for partitioning multi-view data, especially multi-view high-dimensional data. Despite great success, most of the existing multi-view subspace clustering methods still suffer from three limitations. First, they often recover the subspace structure in the original space, which can not guarantee the robustness when handling multi-view data with nonlinear structure. Second, these methods mostly regard subspace clustering and affinity matrix learning as two independent steps, which may not well discover the latent relationships among data samples. Third, many of them ignore the different importance of multiple views, whose performance may be badly affected by the low-quality views in multi-view data. To overcome these three limitations, this paper develops a novel subspace clustering method for multi-view data, termed Kernelized Multi-view Subspace Clustering via Auto-weighted Graph Learning (KMSC-AGL). Specifically, the proposed method implicitly maps the multi-view data from linear space into nonlinear space via kernel-induced functions, so as to exploit the nonlinear structure hidden in data. Furthermore, our method aims to enhance the clustering performance by learning a set of view-specific representations and their affinity matrix in a general framework. By integrating the view weighting strategy into this framework, our method can automatically assign the weights to different views, while learning an optimal affinity matrix that is well-adapted to the subsequent spectral clustering. Extensive experiments are conducted on a variety of multi-view data sets, which have demonstrated the superiority of the proposed method.

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Notes

  1. https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html

  2. http://archive.ics.uci.edu/ml/datasets/Multiple+Features

  3. http://www.uk.research.att.com/facedatabase.html

  4. http://vision.ucsd.edu/~iskwak/ExtYaleDatabase/Yale$%$20Face$ %$20Database.htm

  5. The improvement is computed by \(\frac {\mathrm {The KMSC-AGL result - The best baseline result}}{\mathrm {The best baseline result}} \times 100\% \).

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Acknowledgments

The authors would like to thank the anonymous reviewers and the Associate Editor for their constructive comments and suggestions, which greatly improve this paper. This work was supported by the NSFC (61773410, 61673403, 61976097, 62006252).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Guang-Yu Zhang, Xiao-Wei Chen, Yu-Ren Zhou, Chang-Dong Wang & Xiao-Yu He

  2. Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, Guangzhou, People’s Republic of China

    Yu-Ren Zhou

  3. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, People’s Republic of China

    Dong Huang

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  1. Guang-Yu Zhang
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  2. Xiao-Wei Chen
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Correspondence to Yu-Ren Zhou.

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Zhang, GY., Chen, XW., Zhou, YR. et al. Kernelized multi-view subspace clustering via auto-weighted graph learning. Appl Intell 52, 716–731 (2022). https://doi.org/10.1007/s10489-021-02365-8

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