# Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data

arXiv: https://arxiv.org/abs/2211.14144 · HTML: https://mlsanity.com/paper/2211.14144/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [DeepPINK: reproducible feature selection in deep neural networks](https://mlsanity.com/paper/1809.01185/) (2018)
- [Feature Importance Ranking for Deep Learning](https://mlsanity.com/paper/2010.08973/) (2020)
- [AFS: An Attention-based mechanism for Supervised Feature Selection](https://mlsanity.com/paper/1902.11074/) (2019)
- [Kernel Feature Selection via Conditional Covariance Minimization](https://mlsanity.com/paper/1707.01164/) (2017)
- [Deep Feature Selection using a Teacher-Student Network](https://mlsanity.com/paper/1903.07045/) (2019)
- [Gradient Boosted Feature Selection](https://mlsanity.com/paper/1901.04055/) (2019)
- [A Survey on Hyperlink Prediction](https://mlsanity.com/paper/2207.02911/) (2022)
- [Whiteout: Gaussian Adaptive Noise Regularization in Deep Neural Networks](https://mlsanity.com/paper/1612.01490/) (2016)
- [Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data](https://mlsanity.com/paper/1608.04048/) (2016)
- [Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks](https://mlsanity.com/paper/2204.01682/) (2022)

## Built on by (most cited first, 5 of 5)

- [Feature Selection as Deep Sequential Generative Learning](https://mlsanity.com/paper/2403.03838/) (2024)
- [Attention-based graph neural networks: a survey](https://mlsanity.com/paper/2605.08679/) (2026)
- [Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization](https://mlsanity.com/paper/2403.14688/) (2024)
- [Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Data](https://mlsanity.com/paper/2510.02835/) (2025)
- [MAFS: Multi-head Attention Feature Selection for High-Dimensional Data via Deep Fusion of Filter Methods](https://mlsanity.com/paper/2601.02668/) (2026)

## Abstract (arXiv)

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.