Adaptive Hypergraph Learning for Unsupervised Feature Selection
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Abstract In this paper, we propose a new unsupervised feature selection method to jointly learn the similarity matrix and conduct both subspace learning (via learning a dynamic hypergraph) and feature selection (via a sparsity constraint). As a result, we reduce the feature dimensions using different methods (i.e., subspace learning and feature selection) from different feature spaces, and thus makes our method select the informative features effectively and robustly. Experimental results show that our proposed method outperforms all the comparison methods in terms of clustering tasks.
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