Graph-based semi-supervised learning with multi-label

Abstract

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multi-label. The proposed approach is characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph. We apply the proposed framework to video annotation and report superior performance compared to key existing approaches over the TRECVID 2006 corpus.