通过图随机游走对齐特征与标签空间，提升多标签特征选择效果。
Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
- 构建融合三类关系的图结构，捕捉非线性隐含关联。
- 在低秩分解后优化表示，使特征与标签空间对齐。
- 在7个基准数据集上表现优于现有方法，适合高维多标签场景。
多标签数据中特征维度快速增长带来了特征与标签间的隐含关联，使二者关系日益复杂。现有方法多采用低维线性分解，难以捕捉复杂的非线性关联，且易导致特征空间与标签空间错位。为此，本文提出新方案：首先设计包含特征-特征、标签-标签及特征-标签关系的随机游走图，精准建模非线性与隐式间接关联，并在低秩分解后优化特征-标签关联的潜在表示；其次通过低维表示系数对齐变量空间，同时保留原始高维多标签数据与低维表示空间之间的流形结构。在七个基准数据集和三个代表性数据集上，使用多种评估指标进行的大量实验与消融研究均证明所提方法具有显著优势。代码已公开于https://github.com/Heilong623/-GRW-。
原文摘要 · Abstract (English)
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-}.
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