Continuous label distribution learning

Label distribution learning (LDL) is a suitable paradigm to deal with label ambiguity through learning the correlations among different labels. Most existing label distribution learning methods consider the labels to be discrete and directly establish the mapping from features to labels. However, in many real-world applications, labels naturally form a continuous distribution, which is ignored by the existing methods. As a result, the distribution information of labels can not be accurately described and finally affects the whole learning system. The goal of this paper is to propose a novel approach which can capture the continuous distribution of different labels explicitly and effectively. Specifically, we propose Continuous Label Distribution Learning (CLDL) which describes labels as a continuous density function and learns the distribution information of the labels in the latent space. In this way, the high-order correlations among different labels can be effectively extracted and only a few parameters for describing the continuous distribution need to be learned. Extensive description degree prediction experiments on real-world datasets validate the superiority of CLDL over the existing approaches.