Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data
This addresses overfitting in feature selection for data with many features but few samples, though it appears incremental as a deep learning adaptation of existing techniques.
The paper tackles feature selection for high-dimensional, low-sample size data by proposing GRACES, a graph convolutional network-based method, and shows it outperforms other methods on synthetic and real-world datasets.
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.