Instance-dependent inaccurate label distribution learning

Abstract

Label distribution learning (LDL) is a novel learning paradigm that assigns each instance with a label distribution. Although many specialized LDL algorithms have been proposed, few of them have noticed that the obtained label distributions are generally inaccurate with noise due to the difficulty of annotation. Besides, existing LDL algorithms overlooked that the noise in the inaccurate label distributions generally depends on instances. In this article, we identify the instance-dependent inaccurate LDL (IDI-LDL) problem and propose a novel algorithm called low-rank and sparse LDL (LRS-LDL). First, we assume that the inaccurate label distribution consists of the ground-truth label distribution and instance-dependent noise. Then, we learn a low-rank linear mapping from instances to the ground-truth label distributions and a sparse mapping from instances to the instance-dependent noise. In the theoretical analysis, we establish a generalization bound for LRS-LDL. Finally, in the experiments, we demonstrate that LRS-LDL can effectively address the IDI-LDL problem and outperform existing LDL methods.