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Frobenius norm-regularized robust graph learning for multi-view subspace clustering

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Abstract

Graph learning methods have been widely used for multi-view clustering. However, such methods have the following challenges: (1) they usually perform simple fusion of fixed similarity graph matrices, ignoring its essential structure. (2) they are sensitive to noise and outliers because they usually learn the similarity matrix from the raw features. To solve these problems, we propose a novel multi-view subspace clustering method named Frobenius norm-regularized robust graph learning (RGL), which inherits desirable advantages (noise robustness and local information preservation) from the subspace clustering and manifold learning. Specifically, RGL uses Frobenius norm constraint and adjacency similarity learning to simultaneously explore the global information and local similarity of views. Furthermore, the l2,1 norm is imposed on the error matrix to remove the disturbance of noise and outliers. An effectively iterative algorithm is designed to solve the RGL model by the alternation direction method of multipliers. Extensive experiments on nine benchmark databases show the clear advantage of the proposed method over fifteen state-of-the-art clustering methods.

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Notes

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

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

  3. http://vasc.ri.cmu.edu/idb/html/face/

  4. http://www.cs.columbia.edu/CAVE/software/softlib/

  5. http://research.microsoft.com/en-us/projects/objectclassrecognition/

  6. https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set

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

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 62106063, 62002301, the Guangdong Natural Science Foundation under Grant 2022A1515010819, the Shenzhen College Stability Support Plan under Grant GXWD20201230155427003-20200824113231001, and Chongqing Technology Innovation and Application Demonstration Project under Grant cstc2018jscx-msybX0115.

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Shuqin Wang

  2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, 100044, China

    Shuqin Wang

  3. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Yongyong Chen

  4. College of Criminal Investigation, Southwest University of Political Science and Law, Chongqing, 401120, China

    Shuang Yi

  5. School of Computer Science and Technology, Harbin Institute of Technology, West Culture Road, Weihai, 264209, China

    Guoqing Chao

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  1. Shuqin Wang
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  2. Yongyong Chen
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  3. Shuang Yi
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  4. Guoqing Chao
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Correspondence to Yongyong Chen.

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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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Wang, S., Chen, Y., Yi, S. et al. Frobenius norm-regularized robust graph learning for multi-view subspace clustering. Appl Intell 52, 14935–14948 (2022). https://doi.org/10.1007/s10489-022-03816-6

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