An efficient dictionary-based multi-view learning method

Abstract Multi-view learning can be considered as a kind of classification method which explores common and unique information among different views. For dictionary learning, it can identify informative features by learning sparse representation of samples and has great advantages for classification. However, there are few researches on the problem of multi-view learning with dictionary learning. In order to improve the performance of multi-view classification, we propose a new multi-view dictionary learning with consensus of view(MVDL-CV). First of all, we learn a particular dictionary for each view and obtain the sparse representation of the sample. Then, by utilizing the regularization term between two dictionaries in consensus, we can determine the similarity of samples and obtain the discriminative sparse representation, which can be helpful to construct the improved classifiers. Further, we obtain the solution of the model through an alternating convex optimization method and present the convergence analysis of MVDL-CV. In the experiments, we compare the proposed method with previous multi-view learning methods, and the experimental results show that MVDL-CV is a feasible and competitive method.