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Previous researches have demonstrated that the framework of dictionarylearning with sparse coding, in which signals are decomposed as linearcombinations of a few atoms of a learned dictionary, is well adept toreconstruction issues. This framework has also been used for discriminationtasks such as image classification. To achieve better performances ofclassification, experts develop several methods to learn a discriminativedictionary in a supervised manner. However, another issue is that when the databecome extremely large in scale, these methods will be no longer effective asthey are all batch-oriented approaches. For this reason, we propose a novelonline algorithm for discriminative dictionary learning, dubbed \textbf{ODDL}in this paper. First, we introduce a linear classifier into the conventionaldictionary learning formulation and derive a discriminative dictionary learningproblem. Then, we exploit an online algorithm to solve the derived problem.Unlike the most existing approaches which update dictionary and classifieralternately via iteratively solving sub-problems, our approach directlyexplores them jointly. Meanwhile, it can largely shorten the runtime fortraining and is also particularly suitable for large-scale classificationissues. To evaluate the performance of the proposed ODDL approach in imagerecognition, we conduct some experiments on three well-known benchmarks, andthe experimental results demonstrate ODDL is fairly promising for imageclassification tasks.

Online Discriminative Dictionary Learning for Image Classification Based on Block-Coordinate Descent Method