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Semi-supervised sparse representation collaborative clustering of incomplete data

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Abstract

Sparse subspace clustering (SSC) focuses on revealing the structure and distribution of high dimensional data from an algebraic perspective. It is a two-phase clustering technique, performing sparse representation of the high dimensional data and subsequently cutting the induced affinity graph, which cannot achieve an optimal or expected clustering result. To address this challenge, this paper proposes an approach to subspace representation collaborative clustering (SRCC) for incomplete high dimensional data. In the proposed model, both phases of sparse subspace representation and clustering are integrated into a unified optimization, in which a fuzzy partition matrix is introduced as a bridge to cluster the extracted sparse representation features of the data. At the same time, the missing entries are adaptively imputed along with the two phases. To generalize SRCC to a semi-supervised case, an adjacency matrix of incomplete data is constructed with the ideas of ‘Must-link’ and ‘Cannot-link’. Meanwhile, a semi-supervised indicator matrix is introduced to promote discriminative capacity of revealing global and local structures of incomplete data and enhance the performance of clustering. The semi-supervised sparse representation collaborative clustering (S3RCC) is modeled. Extensive experiments on lots of real-world benchmark datasets demonstrate the superior performance of the proposed two models on imputation and clustering of incomplete data compared to the state-of-the-art methods.

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Notes

  1. The Yale face dataset: http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html

  2. The Extended Yale B (EYB) face dataset: http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html

  3. The JAFFE face dataset: http://www.kasrl.org/jaffe.html

  4. The AR face dataset: http://www2.ece.ohio-state.edu/aleix/ARdatabase.html

  5. The COIL20 object dataset: https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php

  6. The COIL100 object dataset: https://cave.cs.columbia.edu/repository/COIL-100

  7. The Riccardo dataset: https://www.openml.org/search?type=data &status=active &tags.tag=chalearn &id=41161 &sort=runs

  8. The Tomlins-2006-v1 dataset: https://schlieplab.org/Static/Supplements/CompCancer/datasets.htm

  9. The Su-2001 dataset: https://schlieplab.org/Static/Supplements/CompCancer/datasets.htm

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China with grant 12171115.

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  1. College of Mathematical Sciences, Harbin Engineering University, Harbin, 150001, People’s Republic of China

    Tingquan Deng, **gyu Wang, Qingwei Jia & Ming Yang

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  1. Tingquan Deng
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Correspondence to Tingquan Deng.

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Deng, T., Wang, J., Jia, Q. et al. Semi-supervised sparse representation collaborative clustering of incomplete data. Appl Intell 53, 31077–31105 (2023). https://doi.org/10.1007/s10489-023-05168-1

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