Block-based multi-view classification via view-based [formula omitted] sparse representation and adaptive view fusion

Abstract

To date, many efficient classification methods have been presented by utilizing the multi-view data’s rich information. Nevertheless, they commonly construct models by concatenating entire views together into the high-dimensional vectors while ignoring the individuality and relationship of views. Also, they often use fixed labels to perform classification, ignoring the requirement of the large margin between distinct classes. To address the above problems, we propose a new block-based multi-view classification model via view-based L2,p sparse representation and adaptive view fusion. Specifically, the model establishes the L2,p regularization in each view space to excavate the individuality information of views. Meanwhile, a newly proposed shared loss term across views is combined in the model to learn the complementarity and consistency information of views. The adaptive weighting is introduced to measure the contribution of distinct views while performing adaptive view fusion. The model also adopts slack labels to increase the distance of distinct classes. Furthermore, an Alternating Direction Method of Multipliers (ADMM) based algorithm is designed to solve the model through block calculation rapidly. And a strict theoretical proof of its convergence is provided. Extensive experiments demonstrate that the proposed method achieves superior performance.