Class relatedness oriented discriminative dictionary learning

Discriminative dictionary learning (DDL) has recently attracted intensive attention due to its representative and discriminative power in various classification tasks. However, most of the existing DDL methods fall into two extreme cases, i.e., they either learn a global dictionary for all classes or train a class-specific dictionary, leading to less discriminative dictionary as the former do not consider correspondence between dictionary atoms and class labels while the latter ignore dictionary relatedness between different classes. To tackle this issue, in this paper we propose a well-principled DDL method which adaptively builds the relationship between dictionary and class labels. To be specific, we separatively impose a joint sparsity constraint on the coding vectors of each class to learn the class correspondence and relatedness for the dictionary. Experimental results on object classification and face recognition demonstrate that our proposed method can outperform many state-of-the-art DDL methods with more powerful and discriminative dictionary.