Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method

TL;DR

This paper introduces a novel multi-label feature selection method that uses a graph-based random walk to capture complex nonlinear feature-label relationships and aligns feature and label spaces for improved selection accuracy.

Contribution

It proposes a graph random walk approach with feature-label space alignment to better model nonlinear associations in multi-label feature selection.

Findings

Outperforms existing methods on seven benchmark datasets

Effectively captures nonlinear feature-label relationships

Demonstrates robustness across various evaluation metrics

Abstract

The rapid growth in feature dimension may introduce implicit associations between features and labels in multi-label datasets, making the relationships between features and labels increasingly complex. Moreover, existing methods often adopt low-dimensional linear decomposition to explore the associations between features and labels. However, linear decomposition struggles to capture complex nonlinear associations and may lead to misalignment between the feature space and the label space. To address these two critical challenges, we propose innovative solutions. First, we design a random walk graph that integrates feature-feature, label-label, and feature-label relationships to accurately capture nonlinear and implicit indirect associations, while optimizing the latent representations of associations between features and labels after low-rank decomposition. Second, we align the variable spaces by leveraging low-dimensional representation coefficients, while preserving the manifold structure between the original high-dimensional multi-label data and the low-dimensional representation space. Extensive experiments and ablation studies conducted on seven benchmark datasets and three representative datasets using various evaluation metrics demonstrate the superiority of the proposed method\footnote{Code: https://github.com/Heilong623/-GRW-}.