Semi-Supervised Multi-Label Feature Selection with Consistent Sparse Graph Learning
Yan Zhong, Xingyu Wu, Xinping Zhao, Li Zhang, Xinyuan Song, Lei Shi, Bingbing Jiang
TL;DR
This work tackles semi-supervised multi-label feature selection in high-dimensional data by introducing SGMFS, which jointly learns a shared label subspace to capture label correlations and an adaptive sparse graph to preserve space and structure across label and feature spaces. By integrating a soft-label propagation framework with space consistency and a sparsity-constrained reconstruction graph, the method produces reliable unlabeled predictions and a discriminative feature weight matrix $W$ for selection. The authors provide rigorous optimization and convergence guarantees, along with extensive experiments on seven diverse datasets showing superior performance and stability compared to state-of-the-art baselines. The approach offers scalable, robust feature selection for multi-label tasks under incomplete labeling and has potential impact on practical domains requiring efficient, interpretable feature weighting.
Abstract
In practical domains, high-dimensional data are usually associated with diverse semantic labels, whereas traditional feature selection methods are designed for single-label data. Moreover, existing multi-label methods encounter two main challenges in semi-supervised scenarios: (1). Most semi-supervised methods fail to evaluate the label correlations without enough labeled samples, which are the critical information of multi-label feature selection, making label-specific features discarded. (2). The similarity graph structure directly derived from the original feature space is suboptimal for multi-label problems in existing graph-based methods, leading to unreliable soft labels and degraded feature selection performance. To overcome them, we propose a consistent sparse graph learning method for multi-label semi-supervised feature selection (SGMFS), which can enhance the feature selection performance by maintaining space consistency and learning label correlations in semi-supervised scenarios. Specifically, for Challenge (1), SGMFS learns a low-dimensional and independent label subspace from the projected features, which can compatibly cross multiple labels and effectively achieve the label correlations. For Challenge (2), instead of constructing a fixed similarity graph for semi-supervised learning, SGMFS thoroughly explores the intrinsic structure of the data by performing sparse reconstruction of samples in both the label space and the learned subspace simultaneously. In this way, the similarity graph can be adaptively learned to maintain the consistency between label space and the learned subspace, which can promote propagating proper soft labels for unlabeled samples, facilitating the ultimate feature selection. An effective solution with fast convergence is designed to optimize the objective function. Extensive experiments validate the superiority of SGMFS.