Graph convolutional network-based feature selection for high-dimensional and low-sample size data.

Abstract

Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. We present a deep learning-based method-GRAph Convolutional nEtwork feature Selector (GRACES)-to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets. The source code is publicly available at https://github.com/canc1993/graces.