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Adaptive low-rank kernel block diagonal representation subspace clustering

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Abstract

The kernel subspace clustering algorithm aims to tackle the nonlinear subspace model. The block diagonal representation subspace clustering has a more promising capability in pursuing the k-block diagonal matrix. Therefore, the low-rankness and the adaptivity of the kernel subspace clustering can boost the clustering performance, so an adaptive low-rank kernel block diagonal representation (ALKBDR) subspace clustering algorithm is put forward in this work. On the one hand, for the nonlinear nature of the practical visual data, a kernel block diagonal representation (KBDR) subspace clustering algorithm is put forward. The proposed KBDR algorithm first maps the original input space into the kernel Hilbert space which is linearly separable, and next applies the spectral clustering on the feature space. On the other hand, the ALKBDR algorithm uses the adaptive kernel matrix and makes the feature space low-rank to further promote the clustering performance. The experimental results on the Extended Yale B database and the ORL dataset have proved the excellent quality of the proposed KBDR and ALKBDR algorithm in comparison with other advanced subspace clustering algorithms that also are tested in this paper.

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Acknowledgments

This work was supported by the National Key R&D Program of China (Project Number: 2018YFB1701903) and the National Natural Science Foundation of China (Project Numbers: 61973138, 61672263).

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Authors and Affiliations

  1. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, No 1800, Lihu Avenue, Wuxi, Jiangsu, 214122, China

    Maoshan Liu, Yan Wang & Zhicheng Ji

  2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, No 1800, Lihu Avenue, Wuxi, Jiangsu, 214122, China

    Jun Sun

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  1. Maoshan Liu
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  2. Yan Wang
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  3. Jun Sun
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  4. Zhicheng Ji
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Correspondence to Yan Wang.

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Liu, M., Wang, Y., Sun, J. et al. Adaptive low-rank kernel block diagonal representation subspace clustering. Appl Intell 52, 2301–2316 (2022). https://doi.org/10.1007/s10489-021-02396-1

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