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Semi-supervised multi-label feature selection with consistent sparse graph learning.

Yan Zhong¹, Xingyu Wu², Xinping Zhao³

¹School of Mathematical Sciences, Peking University, Beijing, 100871, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 20, 2026

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This summary is machine-generated.

This study introduces a novel sparse graph learning method for multi-label semi-supervised feature selection (SGMFS). SGMFS effectively addresses challenges in learning label correlations and constructing reliable similarity graphs for improved feature selection performance.

Area of Science:

Machine Learning
Data Science
Computer Vision

Background:

High-dimensional data often possess multiple semantic labels, posing challenges for traditional single-label feature selection methods.
Existing multi-label feature selection methods struggle in semi-supervised settings, particularly with limited labeled samples and suboptimal similarity graph construction.

Purpose of the Study:

To propose a consistent sparse graph learning method (SGMFS) for multi-label semi-supervised feature selection.
To enhance feature selection by maintaining space consistency and learning label correlations in semi-supervised scenarios.

Main Methods:

SGMFS learns a low-dimensional label subspace to capture label correlations from projected features.
It adaptively learns a similarity graph by simultaneously performing sparse reconstruction in both the label space and the learned subspace.

Keywords:

Label correlation Multi-label learning Semi-supervised feature selection Sparse graph learning Subspace learning

Related Experiment Videos

An efficient optimization solution with fast convergence is employed.

Main Results:

The proposed SGMFS method effectively addresses the limitations of existing semi-supervised multi-label feature selection techniques.
It demonstrates superior performance in learning label correlations and constructing reliable similarity graphs.
Experimental results validate the effectiveness and superiority of SGMFS.

Conclusions:

SGMFS offers a robust framework for multi-label semi-supervised feature selection.
The method enhances feature selection by improving the handling of label correlations and similarity graph construction.
SGMFS shows significant potential for practical applications involving high-dimensional, multi-label data.