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