Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28742
Title: Sparsity preserving embedding with manifold learning and discriminant analysis
Authors: Liu, Q
Lan, C
Jing, XY
Gao, SQ
Zhang, D 
Yang, JY
Keywords: Discriminant analysis
Feature extraction
Manifold learning
Sparsity preserving embedding
Issue Date: 2012
Source: IEICE Transactions on information and systems, 2012, v. E-95-D, no. 1, p. 271-274 How to cite?
Journal: IEICE Transactions on Information and Systems 
Abstract: 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.
URI: http://hdl.handle.net/10397/28742
ISSN: 0916-8532
DOI: 10.1587/transinf.E95.D.271
Appears in Collections:Conference Paper

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