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Graph constraint-based robust latent space low-rank and sparse subspace clustering

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Abstract

Recently, low-rank and sparse representation-based methods have achieved great success in subspace clustering, which aims to cluster data lying in a union of subspaces. However, most methods fail if the data samples are corrupted by noise and outliers. To solve this problem, we propose a novel robust method that uses the F-norm for dealing with universal noise and the \(l_1\) norm or the \(l_{2,1}\) norm for capturing outliers. The proposed method can find a low-dimensional latent space and a low-rank and sparse representation simultaneously. To preserve the local manifold structure of the data, we have adopted a graph constraint in our model to obtain a discriminative latent space. Extensive experiments on several face benchmark datasets show that our proposed method performs better than state-of-the-art subspace clustering methods.

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Notes

  1. https://www.sheffield.ac.uk/eee/research/iel/research/face.

  2. http://archive.ics.uci.edu/ml/datasets/cmu+face+images.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61402181, 61502174), the Natural Science Foundation of Guangdong Province (2015A030313215, 2017A030313358, 2017A030313355), the Science and Technology Planning Project of Guangdong Province (2016A040403046), the Guangzhou Science and Technology Planning Project (201704030051).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Yunjun Xiao, Jia Wei, Jiabing Wang & Qianli Ma

  2. School of Computing, University of Utah, Salt Lake City, USA

    Shandian Zhe

  3. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA

    Tolga Tasdizen

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  1. Yunjun Xiao
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  2. Jia Wei
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  3. Jiabing Wang
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  4. Qianli Ma
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  5. Shandian Zhe
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Correspondence to Jia Wei.

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Xiao, Y., Wei, J., Wang, J. et al. Graph constraint-based robust latent space low-rank and sparse subspace clustering. Neural Comput & Applic 32, 8187–8204 (2020). https://doi.org/10.1007/s00521-019-04317-3

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