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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:Scopus、EI、CPCI-S

Volume:10559

Page Number:461-470

Key Words:Subspace clustering; Multiplicative noise

Abstract:Traditional subspace clustering models generally adopt the hypothesis of additive noise, which, however, dose not always hold. When it comes to multiplicative noise corruption, these models usually have poor performance. Therefore, we propose a novel model for robust subspace clustering with multiplicative noise corruption to alleviate this problem, which is the key contribution of this work. The proposed model is evaluated on the Extend Yale B and MNIST datasets and the experimental results show that our method achieves favorable performance against the state-of-the-art methods.