A Flexible Dirty Model Dictionary Learning Approach for Classification

Abstract

Various dictionary learning methods have gained tremendous success for signal classification. However, traditional dictionary learning methods for classification assume there is no outlier in the training data, which may not be the case in practical applications. In this paper, we propose a new discriminative dictionary learning framework for classification, which simultaneously learns a discriminative dictionary and detects outliers in the data. We formulate the dictionary learning framework into an optimization problem with designed regularizers to promote both the discrimination and outlier-detection capability. An efficient and effective iterative algorithm based on the alternating direction method of multipliers (ADMM) is provided to solve the proposed optimization problem. We demonstrate the superior performance of the proposed approach in comparison with state-of-the-art methods on some image classification tasks.