Supervised multi-view classification via the sparse learning joint the weighted elastic loss

Abstract

Multi-view data contains the global shared information of all views and the specific information of each view. Usually, the features of all views are concatenated into vectors to be dealt with in the multi-view classification methods, which inevitably ignores the extraction of important views for a specific task and increases the computational complexity. To address the above limitations, we propose a novel supervised classification method via the sparse learning joint the weighted elastic loss. Specifically, for the samples in each class, an elastic sub-model is established by combining the weighted elastic loss and the sparse regularizers. The weighted elastic loss can explore the complementary information of all views and the specific information of each view. The sparse regularizers can select the views that are important to the classification task. Finally, the elastic sub-models of all classes are fused to form the generalized additive classification model. An effective algorithm is designed to optimize the model through solving several small-scale subproblems, which can reduce the computational complexity. The comprehensive evaluations with several advanced methods prove the superiority of the proposed method. Especially on the large-scale Animal dataset, the training time (3.7×103 s) is one time faster than that of the second place (7.5×103 s).