Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10258
Title: Optimal feature selection for robust classification via l2,1-norms regularization
Authors: Wen, J
Lai, Z
Wong, WK 
Cui, J
Wan, M
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - International Conference on Pattern Recognition, 2014, 6976809, p. 517-521 How to cite?
Abstract: This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an alternately iterative algorithm is proposed to optimize both the jointly sparse projection matrix and representation matrix. Experimental results on three public face datasets and one action dataset validate the quick convergence of our algorithm and show that the proposed method is more competitive than the state-of-the-art methods.
Description: 22nd International Conference on Pattern Recognition, ICPR 2014, Sweden, 24-28 August 2014
URI: http://hdl.handle.net/10397/10258
ISBN: 9781479952083
ISSN: 1051-4651
DOI: 10.1109/ICPR.2014.99
Appears in Collections:Conference Paper

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