Topological Information Utilization in Label Enhancement and Label Distribution Learning Based on Optimal Transport Theory

Label Distribution Learning (LDL) offers a promising solution to label ambiguity by employing Label Distributions (LDs) instead of traditional logical labels. However, acquiring LDs for real-world data is both expensive and challenging. To address this issue, Label Enhancement (LE) techniques have been proposed to derive LDs from readily available logical labels. While much of the prior work has focused on enhancing LE for better recovery performance, the ultimate objective remains improving LDL’s overall effectiveness. In this paper, we introduce a novel LE method, Topological Label Enhancement via Optimal Transport (TLEOT), which integrates Optimal Transport (OT) theory with topological space analysis. This method goes beyond improving LE, targeting the enhancement of LDL performance by aligning the feature and label distributions within a unified topological framework. Additionally, we present two innovative topological techniques designed to further improve LDL. Extensive experimental evaluations on real-world datasets demonstrate that TLEOT consistently outperforms nine state-of-the-art methods in predictive tasks. Furthermore, the proposed topological techniques significantly enhance LDL’s performance, validating their practical utility in real-world applications.