Rethinking Label-specific Features for Label Distribution Learning

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.

Rethinking Label-specific Features for Label Distribution Learning

Label Distribution Learning (LDL) represents a significant advance compared to Single-Label Learning (SLL) and Multi-Label Learning (MLL) paradigms. By assigning label distributions to each instance, LDL provides a nuanced representation of label space where description degrees quantify the relevance of each label to given data points. Despite these advantages, existing LDL algorithms commonly overlook label-specific semantic relationships, often relying on uniform feature spaces across all labels. Recognizing the importance of label-specific features (LSFs), offering more discriminative descriptions, this paper introduces the LIFT-SAP strategy for constructing LSFs tailored to LDL tasks.

LIFT-SAP: Enhancing Label-specific Features

The authors propose the LIFT-SAP strategy as an enhancement to the LIFT strategy, leveraging Structural Anchor Points (SAPs) to capture inter-cluster interactions effectively. LIFT works by identifying meaningful prototypes through clustering analysis, re-characterizing instances based on distance from these prototypes. However, relying solely on intra-cluster relationships and Euclidean distances introduces noise and bias, potentially limiting effectiveness in LDL tasks characterized by label ambiguity.

The LIFT-SAP strategy involves structural extension where SAPs serve as midpoints between prototype pairs within instance clusters, pooling both distance and direction information. By considering both distance and angular metrics, LIFT-SAP provides a robust and comprehensive representation of data points, thus capturing label-specific characteristics while mitigating noise. Each LSF space enriched by LIFT-SAP has been demonstrated to provide a more discriminative feature space for learning label distributions.

LDL-LIFT-SAP: Integrating Predictions Across Feature Spaces

Given the transformational construct of LSF spaces, the development of a corresponding LDL algorithm, LDL-LIFT-SAP, was necessary to capitalize on multilayered feature spaces. By independently constructing LSF spaces for each label and integrating multiple label description degree predictions, LDL-LIFT-SAP unifies these variations into one cohesive prediction, achieving highly discriminative label distribution learning.

Extensive experimental validations across 15 LDL datasets show that LDL-LIFT-SAP outperforms state-of-the-art algorithms such as LALOT, LDLLC, EDL-LRL, and others. This superiority is particularly pronounced in image-related LDL tasks, highlighting its potential to handle complex data representations effectively.

Implications and Future Directions

From a theoretical standpoint, this paper guides how structural properties and multi-perspective feature characterizations enhance label distribution tasks learning. Practically, the proposed LIFT-SAP and LDL-LIFT-SAP indicate directions for optimizing LDL approaches in tasks involving dense feature representations, such as bioinformatics or facial expression recognition.

Future developments could explore deeper integration frameworks within LDL, capitalizing on relationships and dependencies among label features to refine predictions further. Another avenue may involve the construction of SAPs using more complex metrics or employing advanced clustering techniques to enhance the capture of intra- and inter-cluster relationships.

The ideas presented in this study not only lay the groundwork for improving LDL but also highlight the importance of generating discriminative feature representations and robust modeling approaches, applicable in numerous real-world scenarios.