Sparsity Preserving Embedding with Manifold Learning and Discriminant Analysis

Qian LIU  Chao LAN  Xiao Yuan JING  Shi Qiang GAO  David ZHANG  Jing Yu YANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.1   pp.271-274
Publication Date: 2012/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.271
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
sparsity preserving embedding,  manifold learning,  discriminant analysis,  feature extraction,  

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In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.