Label Distribution Learning Based on Horizontal and Vertical Mining of Label Correlations

Abstract

Label distribution learning (LDL) is a novel approach that outputs labels with varying degrees of description. To enhance the performance of LDL algorithms, researchers have developed different algorithms with mining label correlations globally, locally, and both globally and locally. However, existing LDL algorithms for mining local label correlations roughly assume that samples within a cluster share same label correlations, which may not be applicable to all samples. Moreover, existing LDL algorithms apply global and local label correlations to the same parameter matrix, which cannot fully exploit their respective advantages. To address these issues, a novel LDL method based on horizontal and vertical mining of label correlations (LDL-HVLC) is proposed in this paper. The method first encodes a unique local influence vector for each sample through the label distribution of its neighbor samples. Then, this vector is extended as additional features to assist in predicting unknown instances, and a penalty term is designed to correct wrong local influence vector (horizontal mining). Finally, to capture both local and global correlations of label, a new regularization term is constructed to constrain the global label correlations on the output results (vertical mining). Extensive experiments on real datasets demonstrate that the proposed method effectively solves the label distribution problem and outperforms the current state-of-the-art methods.