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Sparse sample self-representation for subspace clustering

  • Recent advances in Pattern Recognition and Artificial Intelligence
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Abstract

This paper proposes a new subspace clustering method based on sparse sample self-representation (SSR). The proposed method considers SSR to solve the problem that affinity matrix does not strictly follow the structure of subspace, and also utilizes sparse constraint to ensure the robustness to noise and outliers in subspace clustering. Specifically, we propose to first construct a self-representation matrix for all samples and combine an l 1-norm regularizer with an l 2,1-norm regularizer to guarantee that each sample can be represented as a sparse linear combination of its related samples. Then, we conduct the resulting matrix to build an affinity matrix. Finally, we apply spectral clustering on the affinity matrix to conduct clustering. In order to validate the effectiveness of the proposed method, we conducted experiments on UCI datasets, and the experimental results showed that our proposed method reduced the minimal clustering error, outperforming the state-of-the-art methods.

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Acknowledgments

This work was supported in part by the China 973 Program under Grant 2013CB329404, in part by the National Natural Science Foundation of China under Grant Nos. 61450001, 61263035 and 61573270, in part by the Guangxi Natural Science Foundation under Grant Nos. 2012GXNSFGA060004 and 2015GXNSFCB139011, in part by the China Postdoctoral Science Foundation under Grant 2015M57570837, in part by the Guangxi Higher Institutions’ Program of Introducing 100 High-Level Overseas Talents, in part by the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing and in part by the Guangxi Bagui Scholar Teams for Innovation and Research Project.

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security, College of Computer Science & Information, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Zhenyun Deng, Shichao Zhang, Lifeng Yang, Ming Zong & Debo Cheng

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  1. Zhenyun Deng
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  2. Shichao Zhang
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  3. Lifeng Yang
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  4. Ming Zong
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  5. Debo Cheng
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Correspondence to Shichao Zhang.

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Cite this article

Deng, Z., Zhang, S., Yang, L. et al. Sparse sample self-representation for subspace clustering. Neural Comput & Applic 29, 43–49 (2018). https://doi.org/10.1007/s00521-016-2352-2

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  • DOI: https://doi.org/10.1007/s00521-016-2352-2

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