Discriminative Analysis Dictionary and Classifier Learning for Pattern Classification

Abstract

Sparse representation (SR) and dictionary learning (DL) have been widely used to encode the feature data and facilitate pattern classification. Existing methods generally use l0/l1 norm or class-specific dictionary to enforce the class discriminative ability of the SR. The resulted class discriminative ability is limited. In this work, we propose to use the training set as the synthesis dictionary for SR of the training samples because it provides the most natural class-specific dictionary. The class information of the training set can be used to enhance an ideal discriminative property of the SR: exact block diagonal structure, meaning that each data can be represented only by datain-class. To make the test stage easy, an analysis dictionary and a linear classifier are learnt under the supervision of the discriminative SR of the training set. Once the analysis dictionary and the classifier are learnt, the test stage is very simple and computation efficient. We call our method Discriminative Analysis Dictionary and Classifier Learning (DADCL). Extensive experiments show that our method outperforms some existing state-of-the-art methods.