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When Features Become Instances: Inverted Contrastive Learning for Unsupervised Feature Selection
Unsupervised feature selection seeks a compact subset of informative features without access to class labels, making feature utility difficult to define. Existing UFS methods therefore rely on indirect structural criteria, such as similarity preservation, locality, sparsity, cluster geometry, or rec
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