SEHFS: Structural Entropy-Guided High-Order Correlation Learning for Multi-View Multi-Label Feature Selection

2026-03-03

AI Summary

Existing methods struggle to effectively model high-order structural correlations in multi-view multi-label data and are prone to local optima. This work proposes a novel framework that, for the first time, integrates structural entropy into this task by unifying information-theoretic principles with matrix factorization. Specifically, it constructs a minimum structural entropy encoding tree to quantify the information cost of high-order dependencies, clusters redundant features, and minimizes inter-cluster correlations. Simultaneously, it jointly learns shared semantic and view-specific contribution matrices, thereby balancing global and local optimization. Extensive experiments on eight cross-domain datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches, and ablation studies confirm the effectiveness of each component.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In recent years, multi-view multi-label learning (MVML) has attracted extensive attention due to its close alignment to real-world scenarios. Information-theoretic methods have gained prominence for learning nonlinear correlations. However, two key challenges persist: first, features in real-world data commonly exhibit high-order structural correlations, but existing information-theoretic methods struggle to learn such correlations; second, commonly relying on heuristic optimization, information-theoretic methods are prone to converging to local optima. To address these two challenges, we propose a novel method called Structural Entropy Guided High-Order Correlation Learning for Multi-View Multi-Label Feature Selection (SEHFS). The core idea of SEHFS is to convert the feature graph into a structural-entropy-minimizing encoding tree, quantifying the information cost of high-order dependencies and thus learning high-order feature correlations beyond pairwise correlations. Specifically, features exhibiting strong high-order redundancy are grouped into a single cluster within the encoding tree, while inter-cluster feaeture correlations are minimized, thereby eliminating redundancy both within and across clusters. Furthermore, a new framework based on the fusion of information theory and matrix methods is adopted, which learns a shared semantic matrix and view-specific contribution matrices to reconstruct a global view matrix, thereby enhancing the information-theoretic method and balancing the global and local optimization. The ability of structural entropy to learn high-order correlations is theoretically established, and and both experiments on eight datasets from various domains and ablation studies demonstrate that SEHFS achieves superior performance in feature selection.

Problem

• multi-view multi-label learning
• high-order correlation
• feature selection
• information-theoretic methods
• structural entropy

Innovation

• Structural Entropy
• High-Order Correlation
• Multi-View Multi-Label Feature Selection
• Information-Theoretic Optimization
• Encoding Tree