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Efficient subspace clustering based on self-representation and grouping effect

  • Recent advances in Pattern Recognition and Artificial Intelligence
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Abstract

Traditional subspace clustering methods [such as sparse subspace clustering (SSC), least squares representation (LSR) and smooth representation clustering] either considered the grouping effect or the sparsity to group original data into clusters. This paper demonstrates the necessary of both the grouping effect and the sparsity for conducting subspace clustering, by proposing a new Self-Representation and Subspace Clustering based on Grouping Effect (SRGE) method. Specifically, first of all, a row sparse \(\ell_{2,1}\)-norm regularizer is utilized to represent each sample by other samples. Then, the grouping effect of the data is designed to ensure that the coefficient of close samples is similar, aiming at generating a diagonal block self-representation coefficient matrix. Finally, an affinity matrix is obtained for conducting spectral clustering. The proposed method can be regarded as a trade-off between SSC and LSR. The experimental results of segmentation on real datasets showed that the proposed method significantly outperformed the state-of-the-art methods in terms of all kinds of evaluation metrics.

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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 61450001, Grant 61263035 and Grant 61573270, in part by the Guangxi Natural Science Foundation under Grant 2012GXNSFGA060004 and Grant 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, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Shichao Zhang, Yonggang Li, Debo Cheng, Zhenyun Deng & Lifeng Yang

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

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Zhang, S., Li, Y., Cheng, D. et al. Efficient subspace clustering based on self-representation and grouping effect. Neural Comput & Applic 29, 51–59 (2018). https://doi.org/10.1007/s00521-016-2353-1

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