Learning a discriminative dictionary for classification with outliers

Dictionary learning aims to find a dictionary where signals in some ensemble have sparse representations, and has been successfully applied for 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. The dictionary learning framework is formulated into an optimization problem with designed regularizers to promote both the discrimination and outlier-detection capability. We demonstrate the superior performance of the proposed approach in comparison with state-of-the-art alternatives by conducting extensive experiments on various image classification tasks.