mlsanityData to 13 Oct 2026

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

arXiv 2211.14144 ↗First posted 25 Nov 2022cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 14 of its 59 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

14 of 59 references.

References · 14

  1. 2017Inductive Representation Learning on Large Graphscited by 6,303
  2. 2017Learning Important Features Through Propagating Activation Differencescited by 1,455
  3. 2018Hypergraph Neural Networkscited by 485
  4. 2019Hypergraph Convolution and Hypergraph Attentioncited by 227
  5. 2018DeepPINK: reproducible feature selection in deep neural networkscited by 41
  6. 2020Feature Importance Ranking for Deep Learningcited by 31
  7. 2019AFS: An Attention-based mechanism for Supervised Feature Selectioncited by 21
  8. 2017Kernel Feature Selection via Conditional Covariance Minimizationcited by 20
  9. 2019Deep Feature Selection using a Teacher-Student Networkcited by 16
  10. 2019Gradient Boosted Feature Selectioncited by 13
  11. 2022A Survey on Hyperlink Predictioncited by 9
  12. 2016Whiteout: Gaussian Adaptive Noise Regularization in Deep Neural Networkscited by 8
2 more
  1. 2016Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Datacited by 6
  2. 2022Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networkscited by 6

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2024Feature Selection as Deep Sequential Generative Learningcited by 21
  2. 2026Attention-based graph neural networks: a surveycited by 1
  3. 2024Kernel Alignment for Unsupervised Feature Selection via Matrix Factorizationcited by 0
  4. 2025Subject-Adaptive Sparse Linear Models for Interpretable Personalized Health Prediction from Multimodal Lifelog Datacited by 0
  5. 2026MAFS: Multi-head Attention Feature Selection for High-Dimensional Data via Deep Fusion of Filter Methodscited by 0