Label distribution learning and label enhancement

Abstract

This paper introduces the concepts and algorithms for label distribution learning (LDL) and label enhancement. LDL is a general machine learning paradigm with traditional single-label learning and multi-label learning as its special cases. A label distribution covers a certain number of labels, representing the degree to which each label describes the instance. Thus, LDL has been successfully applied to many real-world problems. Unfortunately, many existing datasets only have simple logical labels rather than label distributions. One way to solve the problem is to transform the logical labels into label distributions by mining the latent label importance from the training examples. Such a process of transforming logical labels into label distributions is defined as label enhancement. This paper provides formal definitions of label distribution learning and label enhancement. Subsequently, six representative LDL algorithms and four typical LE algorithms are briefly introduced and comparatively analyzed.