Dictionary learning in optimal metric space

Abstract

Dictionary learning has been widely used in machine learning field to address many real-world applications, such as classification and denoising. In recent years, many new dictionary learning methods have been proposed. Most of them are designed to solve unsupervised problem without any prior information or supervised problem with the label information. But in real world, as usual, we can only obtain limited side information as prior information rather than label information. The existing methods don't take into account the side information, let alone learning a good dictionary through using the side information. To tackle it, we propose a new unified unsupervised model which naturally integrates metric learning to enhance dictionary learning model with fully utilizing the side information. The proposed method updates metric space and dictionary adaptively and alternatively, which ensures learning optimal metric space and dictionary simultaneously. Besides, our method can also deal well with high-dimensional data. Extensive experiments show the efficiency of our proposed method, and a better performance can be derived in real-world image clustering applications.