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Multi-label feature selection via binary label subspace learning and hypergraph constraints
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DOI：10.1016/j.engappai.2026.114574.png)
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Abstract
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• A new multi-label feature selection method utilizing binary label subspace and hypergraph constraints is proposed. • The proposed algorithm uses a new label subspace method to refine label representation. • hypergraph constraints are used to explore complex high-order correlations. • Comparison results on 10 benchmark datasets show that the proposed method achieves superior performance.
Keywords:
Multi-label feature selection
Binary label subspace
Hypergraph constraints
Label representation
High-order correlations
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