Feature extraction with sparsity preserving laplacian discriminant analysis

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作者单位: 314001, China ) CAD Research Center, Tongji University, Shanghai School of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing 200092, China 271016, China College of Information Engineering, Taishan Medical University, Taian

摘要: Aiming at the unsupervised and time-consuming l1norm optimization problems of the existing sparsity preserving projection, a novel fast feature extraction algorithm named sparsity preserving laplacian discriminant analysis is proposed. SPLDA first creates a concatenated dictionary via class-wise principal component analysis decompositions and learns the sparse representation structure of each sample under the dictionary using the least square method. Then SPLDA considers both the sparse repres...更多; Aiming at the unsupervised and time-consuming l1norm optimization problems of the existing sparsity preserving projection, a novel fast feature extraction algorithm named sparsity preserving laplacian discriminant analysis is proposed. SPLDA first creates a concatenated dictionary via class-wise principal component analysis decompositions and learns the sparse representation structure of each sample under the dictionary using the least square method. Then SPLDA considers both the sparse representation structure and the discriminative efficiency by regularizing the Laplacian discriminant function from the learned sparse representation structure. Finally, the proposed method is transformed into a generalized eigenvalue problem. Extensive experiments on several popular face databases and Extended Yale B) are provided to validate the feasibility and effectiveness of the proposed algorithm.收起

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