Exploiting Multi-Label Correlation in Label Distribution Learning

• URL: http://arxiv.org/abs/2308.01742v1
• Date: Thu, 3 Aug 2023 13:06:45 GMT
• Title: Exploiting Multi-Label Correlation in Label Distribution Learning
• Authors: Zhiqiang Kou jing wang yuheng jia xin geng
• Abstract summary: Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to works exploiting low-rank label correlation. We introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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