Distance-Preserving Embedding Adaptive Bipartite Graph Multi-View Learning with Application to Multi-Label Classification

Abstract

Graph-based multi-view learning has attracted much attention due to the efficacy of fusing the information from different views. However, most of them exhibit high computational complexity. We propose an anchor-based bipartite graph embedding approach to accelerate the learning process. Specifically, different from existing anchor-based methods where anchors are obtained from key samples by clustering or weighted averaging strategies, in this article, the anchors are learned in a principled fashion which aims at constructing a distance-preserving embedding for each view from samples to their representations, whose elements are the weights of the edges linking corresponding samples and anchors. In addition, the consistency among different views can be explored by imposing a low-rank constraint on the concatenated embedding representations. We further design a concise yet effective feature collinearity guided feature selection scheme to learn tight multi-label classifiers. The objective function is optimized in an alternating optimization fashion. Both theoretical analysis and experimental results on different multi-label image datasets verify the effectiveness and efficiency of the proposed method.