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Locality and similarity preserving embedding for feature selection
2014
Neurocomputing
Features selection (FS) methods have commonly been used as a main way to select the relevant features. In this paper, we propose a novel unsupervised FS method, i.e., locality and similarity preserving embedding (LSPE) for feature selections. Specifically, the nearest neighbor graph is firstly constructed to preserve the locality structure of data points, and then this locality structure is mapped to the reconstruction coefficients such that the similarity among these data points is preserved.
doi:10.1016/j.neucom.2013.08.040
fatcat:ujgaj3dcqvf4bgjtbaq2f6wgry