Sparsity Preserving Embedding with Manifold Learning and Discriminant Analysis
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.1 pp.271-274
- Publication Date
- 2012/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.271
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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Qian LIU, Chao LAN, Xiao Yuan JING, Shi Qiang GAO, David ZHANG, Jing Yu YANG, "Sparsity Preserving Embedding with Manifold Learning and Discriminant Analysis" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 1, pp. 271-274, January 2012, doi: 10.1587/transinf.E95.D.271.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.271/_p
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@ARTICLE{e95-d_1_271,
author={Qian LIU, Chao LAN, Xiao Yuan JING, Shi Qiang GAO, David ZHANG, Jing Yu YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Sparsity Preserving Embedding with Manifold Learning and Discriminant Analysis},
year={2012},
volume={E95-D},
number={1},
pages={271-274},
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.},
keywords={},
doi={10.1587/transinf.E95.D.271},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Sparsity Preserving Embedding with Manifold Learning and Discriminant Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 271
EP - 274
AU - Qian LIU
AU - Chao LAN
AU - Xiao Yuan JING
AU - Shi Qiang GAO
AU - David ZHANG
AU - Jing Yu YANG
PY - 2012
DO - 10.1587/transinf.E95.D.271
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2012
AB - 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.
ER -
English
