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Journal of Electronic Imaging

Efficient algorithm for sparse coding and dictionary learning with applications to face recognition
Author(s): Zhong Zhao; Guocan Feng
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Paper Abstract

Sparse representation has been successfully applied to pattern recognition problems in recent years. The most common way for producing sparse coding is to use the l1-norm regularization. However, the l1-norm regularization only favors sparsity and does not consider locality. It may select quite different bases for similar samples to favor sparsity, which is disadvantageous to classification. Besides, solving the l1-minimization problem is time consuming, which limits its applications in large-scale problems. We propose an improved algorithm for sparse coding and dictionary learning. This algorithm takes both sparsity and locality into consideration. It selects part of the dictionary columns that are close to the input sample for coding and imposes locality constraint on these selected dictionary columns to obtain discriminative coding for classification. Because an analytic solution of the coding is derived by only using part of the dictionary columns, the proposed algorithm is much faster than the l1-based algorithms for classification. Besides, we also derive an analytic solution for updating the dictionary in the training process. Experiments conducted on five face databases show that the proposed algorithm has better performance than the competing algorithms in terms of accuracy and efficiency.

Paper Details

Date Published: 10 March 2015
PDF: 15 pages
J. Electron. Imaging. 24(2) 023009 doi: 10.1117/1.JEI.24.2.023009
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Zhong Zhao, Sun Yat-Sen Univ. (China)
Guangdong Province Key Lab. of Computational Science (China)
Guocan Feng, Sun Yat-Sen Univ. (China)
Guangdong Province Key Laboratory of Computational Science (China)

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