Inaccurate Label Distribution Learning

Zhiqiang Kou, Jing Wang, Yuheng Jia, Xin Geng

Abstract: Label distribution learning (LDL) trains a model to predict the relevance of a set of labels (called label distribution (LD)) to an instance. The previous LDL methods all assumed the LDs of the training instances are accurate. However, annotating highly accurate LDs for training instances is time-consuming and extremely expensive, and in reality the collected LDs are often inaccurate. This paper first investigates the inaccurate LDL (ILDL) problem—learn an LDL method from the inaccurate LDs. We assume that the inaccurate LD blends the ground-truth LD and sparse noise. Consequently, the ILDL problem becomes an inverse problem, whose objective is to recover the ground-truth LD and noise from the inaccurate LD. We hypothesize that the ground-truth LD exhibits low rank due to label correlations. Besides, we leverage the local geometric structure of instances (represented as graph) to further recover the ground-truth LD. Finally, the proposed method is formulated as a graph-regularized low-rank and sparse decomposition problem. Next, we induce an LDL predictive method by learning from recovered LD. Extensive experiments conducted on multiple datasets demonstrate the better performance of our method, especially for ILDL problem.