Robust block sparse discriminative classification framework

In this paper, a block sparse discriminative classification framework (BSDC) is proposed under the assumption that a block or group structure exists in sparse coefficients on classification. First, we propose a block discriminative dictionary-learning (BDDL) algorithm, which learns class-specific su...

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Bibliographic Details
Published in:Journal of the Optical Society of America. A, Optics, image science, and vision Optics, image science, and vision, 2014-12, Vol.31 (12), p.2806-2813
Main Authors: Liu, Yang, Liu, Chenyu, Tang, Yufang, Liu, Haixu, Ouyang, Shuxin, Li, Xueming
Format: Article
Language:English
Subjects:
Algorithms
Blocking
Classification
Coding
Face recognition
Least squares method
Strategy
Surface layer
Texture
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Summary:In this paper, a block sparse discriminative classification framework (BSDC) is proposed under the assumption that a block or group structure exists in sparse coefficients on classification. First, we propose a block discriminative dictionary-learning (BDDL) algorithm, which learns class-specific subdictionaries and forces the sparse coefficients to be block sparse. An efficient gradient-based optimization strategy of BDDL also is developed, and the block sparse constraint of the sparse coefficient leads to a least-squares solution of nonzero entries in the sparse coding stage of dictionary learning. Second, to take advantage of the structures when a new test sample is given, conventional sparse coding algorithms are discarded, and structured sparse coding methods are adopted. Experiments validate the effectiveness of the proposed framework in face recognition and texture classification. We also show that BSDC is robust to noise.
ISSN:1084-7529
1520-8532
DOI:10.1364/JOSAA.31.002806