Graph Regularized Autoencoder-Based Unsupervised Feature Selection

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, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning, with spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space.