Exploiting Multi-Label Correlation in Label Distribution Learning
Zhiqiang Kou jing wang yuheng jia xin geng
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.