Efficient Graph Based Multi-view Learning

Graph-based learning methods especially multi-graph-based methods have attracted considerable research interests in the past decades. In these methods, the traditional graph models are used to build adjacency relationships for samples within different views. However, owing to the huge time complexity, they are inefficient for large-scale datasets. In this paper, we propose a method named multi-anchor-graph learning (MAGL), which aims to utilize anchor graphs for the adjacency estimation. MAGL can not only sufficiently explore the complementation of multiple graphs built upon different views but also keep an acceptable time complexity. Furthermore, we show that the proposed method can be implemented through an efficient iterative process. Extensive experiments on six publicly available datasets have demonstrated both the effectiveness and efficiency of our proposed approach.