Joint Structured Graph Learning and Unsupervised Feature Selection
Published: May 1, 2019
Abstract
The central task in graph-based unsupervised feature selection (GUFS) depends on two folds, one is to accurately characterize the geometrical structure of the original feature space with a graph and the other is to make the selected features well preserve such intrinsic structure. Currently, most of the existing GUFS methods use a two-stage strategy which constructs graph first and then perform feature selection on this fixed graph. Since the...
Paper Details
Title
Joint Structured Graph Learning and Unsupervised Feature Selection
Published Date
May 1, 2019
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