Geometric Mean Metric Learning for Label Distribution Learning

Abstract

Label distribution learning is an extend multi-label learning paradigm, especially it can preserve the significance of the labels and the related information among the labels. Many studies have shown that label distribution learning has important applications in label ambiguity. However, some classification information in the labels is not effectively utilized. In this paper, we use the classification information in the labels, and combine with the geometric mean metric learning to learn a new metric in the feature space. Under the new metric, the similar samples of the label space are as close as possible, and dissimilar samples are as far as possible. Finally, the GMML-kLDL model is proposed by combining the classification information in the labels and the neighbor information in the features. The experimental results show that the model is effective in label distribution learning and can effectively improve the prediction performance.