Unsupervised feature selection by combining subspace learning with feature self-representation

Abstract So far, most of existing feature selection methods have two defects: 1) These methods are more or less heavy workload, 2) Their effect is not very good enough. To solve the above issues, a novel unsupervised feature selection algorithm is proposed. Specifically, the proposed method uses the property of the data to construct self-representation coefficient matrix, and utilizes sparse representation to find the sparse structure of the self-representation coefficient matrix, and embeds a hypergraph Laplacian regularization term to make up the insignificance of ordinary graph in the representation of multiple relations. The Linear Discriminant Analysis (LDA) algorithm is used to further adjust the result of feature selection. Finally, a low rank constraint is used to capture global structure of data. Experimental results on real datasets showed that the proposed method outperformed the state-of-the-art methods.