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论文类型：会议论文

发表时间：2021-01-10

卷号：11854

页面范围：296-309

关键字：Dimension reduction; Multi-view learning; Co-regularization

摘要：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.