Label Distribution Learning by Optimal Transport
Keywords:machine learning, optimal transport, label distribution learning
Label distribution learning (LDL) is a novel learning paradigm to deal with some real-world applications, especially when we care more about the relative importance of different labels in description of an instance. Although some approaches have been proposed to learn the label distribution, they could not explicitly learn and leverage the label correlation, which plays an importance role in LDL. In this paper, we proposed an approach to learn the label distribution and exploit label correlations simultaneously based on the Optimal Transport (OT) theory. The problem is solved by alternatively learning the transportation (hypothesis) and ground metric (label correlations). Besides, we provide perhaps the first data-dependent risk bound analysis for label distribution learning by Sinkhorn distance, a commonly-used relaxation for OT distance. Experimental results on several real-world datasets comparing with several state-of-the-art methods validate the effectiveness of our approach.