  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. In
this paper, we present a deep learning-based method - GRAph Convolutional
nEtwork feature Selector (GRACES) - to select important features for HDLSS
data. We demonstrate empirical evidence that GRACES outperforms other feature
selection methods on both synthetic and real-world datasets.
