Work

Label distribution learning with structured manifold subspace

Year: 2025

Type: article

Abstract: In recent years, label distribution learning (LDL) has been proposed to solve the label ambiguity problem. To alleviate the overwhelming output space of LDL, most of the existing methods explore label correlations at a global or local level through common low-rank assumptions. However, the real-valued label description degrees have more complex correlations, making the low-rank assumption usually not hold. To tackle this issue, the proposed method leverages the label distribution manifold with structured subspaces, aiming for a more compact and accurate output space while preserving the high rank of the label distribution matrix. Specifically, a robust high-order correlation measure for label distributions is defined. Then, we simultaneously infer model parameters and conduct the label subspace clustering through the maximum correlation entropy (MCE) regularization to achieve mutual enhancement. It is further proven that the resulting label correlation matrix exhibits a block-diagonal structure under the assumption of independent subspaces. Extensive experiments on widely used benchmark datasets demonstrate the clear advantages of our proposed algorithm over state-of-the-art LDL methods. The code is accessible at https://github.com/yan-yp/LDL-MCE .

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.131481

Authors: Yaping Yan, Yunlong Tang, Yongxin Jiang, Songlin Du

Institution: Southeast University

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funder: National Natural Science Foundation of China