Weighted multi-view common subspace learning method

Abstract

• A novel multi-view common subspace learning model is proposed. • The model can obtain a unified common subspace by using the discriminant information within and between views. • The discriminant information within and between views can be adjusted by the weighted coefficient. • Our model adopts the maximize scatter difference criterion as the metric between and within the views after projection. How to use multi-view data effectively has become one of the challenging problems in the computer vision community. The existing multi-view learning methods are mainly based on common subspace learning which aim to explore the discriminative information between multi-view data and find its potential common subspace. Most of the existing multi-view subspace learning methods rely on the within-class scatter matrix and between-class scatter matrix while capturing the discriminative information of multiple views. However, these methods just roughly minimize the within-class distance and maximize the between-class distance, and do not make full use of the intra-view and inter-view information. To address this problem, we propose a weighted common subspace learning method, which can effectively adjust the contribution ratio of between-class and within-class information through a weighted parameter, so that an optimized common subspace can be obtained. And we use the maximum scatter difference criterion as the metric of inter-view and intra-view after projection. Extensive experiments on the public data sets show the superiority of this method.