Structure-constrained low-rank and partial sparse representation for image classification

In this paper, a novel Structure-Constrained Low-Rank and Partial Sparse Representation algorithm for image classification is proposed. First, a Structure-Constrained Low-Rank dictionary learning algorithm is proposed, which imposes both structure and low-rank restriction on the coefficient matrix. Second, under the assumption that the representation of test sample is sparse and correlated with the learned representation of training samples, we concatenate training samples and test samples to form a data matrix and find a low-rank and sparse representation of the data matrix over learned dictionary by low-rank matrix recovery technique. Experimental results demonstrate the effectiveness of the proposed algorithm.