Discriminative embedded unsupervised feature selection

Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features are selected out for effective data representation. In this paper, we propose a novel unsupervised feature selection method which discovers and exploits the global information of the data by maximizing distances between samples from different clusters, and preserving the locality of the data by incorporating a Laplacian regularization. Moreover, the proposed method directly ranks the features without any transformation by introducing a simplex-based sparse learning strategy, and enables highly discriminative features to be chosen. Extensive experiments are carried out and the results show effectiveness of the proposed method.