Exploiting Multi-Label Correlation in Label Distribution Learning

TL;DR

This paper introduces a novel approach to Label Distribution Learning by leveraging low-rank label correlation through an auxiliary multi-label learning process, leading to improved performance over existing methods.

Contribution

It proposes exploiting low-rank label correlation via auxiliary multi-label learning to enhance LDL, addressing the challenge of full-rank label distribution matrices.

Findings

Our methods outperform existing LDL techniques.

Exploiting low-rank label correlation improves learning effectiveness.

Ablation studies confirm the benefits of the proposed approach.

Abstract

Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to those works exploiting low-rank label correlation. Note that multi-label is generally low-rank; low-rank label correlation is widely adopted in multi-label learning (MLL) literature. Inspired by that, we introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL. In such a way, low-rank label correlation is appropriately exploited in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.