Rethinking Label-specific Features for Label Distribution Learning

2025-04-27

AI Summary

To address three key limitations in label distribution learning (LDL)—inadequate modeling of label ambiguity, neglect of inter-cluster interactions in existing LIFT methods, and noise susceptibility of Euclidean-distance-based label-specific feature (LSF) construction—this paper proposes LIFT-SAP. First, it introduces structural anchor points (SAPs) to explicitly model cross-cluster semantic correlations. Second, it designs a multi-view LSF construction mechanism that jointly leverages Euclidean distance and directional vectors to enhance representation robustness. Third, it proposes a multi-space prediction ensemble strategy that unifies heterogeneous label description scores into a coherent final label distribution. Evaluated on 15 real-world datasets, LIFT-SAP consistently outperforms the original LIFT and seven state-of-the-art LDL methods, achieving an average 12.3% reduction in Kullback–Leibler divergence (KLD), thereby demonstrating superior effectiveness and generalizability.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting interactions among distinct clusters. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms.

Problem

• Improving label-specific features for label distribution learning
• Addressing inter-cluster interaction neglect in LIFT prototypes
• Enhancing instance representation with multi-perspective SAP integration

Innovation

• Introduces Structural Anchor Points for inter-cluster interactions
• Enhances LIFT with distance and direction information
• Unifies multiple label description degrees into distribution