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As we aim at alleviating the curse of high-dimensionality, subspace learningis becoming more popular. Existing approaches use either information aboutglobal or local structure of the data, and few studies simultaneously focus onglobal and local structures as the both of them contain important information.In this paper, we propose a global and local structure preserving sparsesubspace learning (GLoSS) model for unsupervised feature selection. The modelcan simultaneously realize feature selection and subspace learning. Inaddition, we develop a greedy algorithm to establish a generic combinatorialmodel, and an iterative strategy based on an accelerated block coordinatedescent is used to solve the GLoSS problem. We also provide whole iteratesequence convergence analysis of the proposed iterative algorithm. Extensiveexperiments are conducted on real-world datasets to show the superiority of theproposed approach over several state-of-the-art unsupervised feature selectionapproaches.

Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection