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Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
[article]
2018
arXiv
pre-print
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by
arXiv:1801.02251v2
fatcat:tgtkgizpnzf3xdvgibg3ep54xe