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

arXiv: https://arxiv.org/abs/2603.03022 · HTML: https://mlsanity.com/paper/2603.03022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 55 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [Comprehensive Comparative Study of Multi-Label Classification Methods](https://mlsanity.com/paper/2102.07113/) (2021)
- [Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/2012.10715/) (2020)

## Abstract (arXiv)

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.