Multi-view spectral clustering via robust local subspace learning


Because of the existence of multiple sources of datasets, multi-view clustering has a wide range of applications in data mining and pattern recognition. Multi-view could utilize complementary information that existed in multiple views to improve the performance of clustering. Recently, there have been multi-view clustering methods which improved the performance of clustering to some extent. However, they do not take local representation relationship into consideration and local representation relationship is a crucial technology in subspace learning. To solve this problem, we proposed a novel multi-view clustering algorithm via robust local representation. We consider that all the views are derived from a robust unified subspace and noisy. To get the robust similarity matrix we simultaneously take all the local reconstruction relationships into consideration and use L1-norm to guarantee the sparsity. We give an iteration solution for this problem and give the proof of correctness. We compare our method with a number of classical methods on real-world and synthetic datasets to show the efficacy of the proposed algorithm.

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Corresponding author

Correspondence to Lin Feng.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.


This study funded by National Natural Science Foundation of People’s Republic of China 61173163, 61370200).

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Communicated by A. Di Nola.

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Feng, L., Cai, L., Liu, Y. et al. Multi-view spectral clustering via robust local subspace learning. Soft Comput 21, 1937–1948 (2017).

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  • Multi-view learning
  • Spectral clustering
  • Robust local subspace learning