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Mathematical Problems in Engineering
Volume 2016, Article ID 3824027, 15 pages
http://dx.doi.org/10.1155/2016/3824027
Research Article

A Weighted Block Dictionary Learning Algorithm for Classification

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China

Received 3 April 2016; Accepted 30 June 2016

Academic Editor: Yaguo Lei

Copyright © 2016 Zhongrong Shi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Discriminative dictionary learning, playing a critical role in sparse representation based classification, has led to state-of-the-art classification results. Among the existing discriminative dictionary learning methods, two different approaches, shared dictionary and class-specific dictionary, which associate each dictionary atom to all classes or a single class, have been studied. The shared dictionary is a compact method but with lack of discriminative information; the class-specific dictionary contains discriminative information but consists of redundant atoms among different class dictionaries. To combine the advantages of both methods, we propose a new weighted block dictionary learning method. This method introduces proto dictionary and class dictionary. The proto dictionary is a base dictionary without label information. The class dictionary is a class-specific dictionary, which is a weighted proto dictionary. The weight value indicates the contribution of each proto dictionary block when constructing a class dictionary. These weight values can be computed conveniently as they are designed to adapt sparse coefficients. Different class dictionaries have different weight vectors but share the same proto dictionary, which results in higher discriminative power and lower redundancy. Experimental results demonstrate that the proposed algorithm has better classification results compared with several dictionary learning algorithms.