Sparse subspace learning based redundancy-aware unsupervised feature selection

doi:10.1016/j.patcog.2025.112611

Sparse subspace learning based redundancy-aware unsupervised feature selection

2025-10-21

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DOI：10.1016/j.patcog.2025.112611

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• A min-max subspace learning framework is proposed for unsupervised feature selection. • A low-redundancy learning strategy guided by multi-measure fusion is introduced. • An ADMM-based iterative algorithm is developed to optimize the resulting problem.

Sparse subspace learning based redundancy-aware unsupervised feature selection

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