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Robust Subspace Segmentation via Sparse Relation Representation

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11859))

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Abstract

Spectral clustering based algorithms are powerful tools for solving subspace segmentation problems. The existing spectral clustering based subspace segmentation algorithms use original data matrices to produce the affinity graphs. In real applications, data samples are usually corrupted by different kinds of noise, hence the obtained affinity graphs may not reveal the intrinsic subspace structures of data sets. In this paper, we present the conception of relation representation, which means a point’s neighborhood relation could be represented by the rest points’ neighborhood relations. Based on this conception, we propose a kind of sparse relation representation (SRR) for subspace segmentation. The experimental results obtained on several benchmark databases show that SRR outperforms some existing related methods.

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Notes

  1. 1.

    The reasons why we choose SCLRR for comparison are illustrated as follows: firstly, SCLRR is the generalization of NNLRSR; secondly, both SCLRR and NNLRSR impose the low-rank and sparse constraints on the reconstruction coefficient matrix to hope it could find the local and global structures of data sets.

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Author information

Authors and Affiliations

  1. Shanghai Maritime University, Haigang Avenue 1550, Shanghai, People’s Republic of China

    Lai Wei

  2. Wuhan Digit Engineering Institute, Canglong North Road 709, Wuhan, Hubei, People’s Republic of China

    Hao Liu

Authors
  1. Lai Wei
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  2. Hao Liu
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Corresponding author

Correspondence to Lai Wei .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi’an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi’an, China

    Yanning Zhang

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Wei, L., Liu, H. (2019). Robust Subspace Segmentation via Sparse Relation Representation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-31726-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31725-6

  • Online ISBN: 978-3-030-31726-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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