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Indexed by:Symposium

Date of Publication:2021-01-10

Volume:11854

Page Number:296-309

Key Words:Dimension reduction; Multi-view learning; Co-regularization

Abstract:In this paper, we focus on boosting the subspace learning by exploring the complimentary and compatible information from multiview features. A novel multi-view dimension reduction method is proposed named Multiview Sparsity Preserving Projection (MSPP) for this task. MSPP aims to seek a set of linear transforms to project multiview features into subspace where the sparse reconstructive weights of multi-view features are preserved as much as possible. And the Hilbert Schmidt Independence Criterion (HSIC) is utilized as a dependence term to explore the compatible and complementary information from multiview features. An efficient alternative iterating optimization is presented to obtain the optimal solution of MSPP. Experiments on image datasets and multi-view textual datasets well demonstrate the excellent performance of MSPP.