Graph Regularized Autoencoder-Based Unsupervised Feature Selection

Published: Oct 1, 2018
Abstract
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high-dimensional data in order to eliminate redundancy. Recently, feature selection methods based on sparse learning have attracted significant attention due to their outstanding performance compared with traditional methods that ignore correlation between features. However, they are restricted by design to linear data transformations,...
Paper Details
Title
Graph Regularized Autoencoder-Based Unsupervised Feature Selection
Published Date
Oct 1, 2018
Journal
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