# Deep Feature Selection using a Teacher-Student Network

arXiv: https://arxiv.org/abs/1903.07045 · HTML: https://mlsanity.com/paper/1903.07045/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 17 of 17)

- [AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks](https://mlsanity.com/paper/2105.10190/) (2021)
- [Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks](https://mlsanity.com/paper/2204.01682/) (2022)
- [Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking -- An Application to COVID-19 severity](https://mlsanity.com/paper/2111.09964/) (2021)
- [Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data](https://mlsanity.com/paper/2211.14144/) (2022)
- [Interpretable Deep Learning Methods for Multiview Learning](https://mlsanity.com/paper/2302.07930/) (2023)
- [Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images](https://mlsanity.com/paper/2303.00943/) (2023)
- [Variable selection for nonlinear Cox regression model via deep learning](https://mlsanity.com/paper/2211.09287/) (2022)
- [Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction](https://mlsanity.com/paper/2304.04692/) (2023)
- [LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm](https://mlsanity.com/paper/2402.17120/) (2024)
- [Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?](https://mlsanity.com/paper/2101.09009/) (2021)
- [Deep Feature Selection Using a Novel Complementary Feature Mask](https://mlsanity.com/paper/2209.12282/) (2022)
- [Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting](https://mlsanity.com/paper/2312.17517/) (2023)
- [Enhancing Unsupervised Feature Selection via Double Sparsity Constrained Optimization](https://mlsanity.com/paper/2501.00726/) (2025)
- [Low-rank Dictionary Learning for Unsupervised Feature Selection](https://mlsanity.com/paper/2106.11102/) (2021)
- [Deep Learning for Efficient GWAS Feature Selection](https://mlsanity.com/paper/2312.15055/) (2023)
- [EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features](https://mlsanity.com/paper/2402.05954/) (2024)
- [Learning to Select Features with Manifold Deep Unfolding PCA](https://mlsanity.com/paper/2610.19436/) (2026)

## Abstract (arXiv)

High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the model. In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension. Then a 'student' network (a simple neural network) is used to perform feature selection by minimizing the reconstruction error of low dimensional representation. Although the teacher-student scheme is not new, to the best of our knowledge, it is the first time that this scheme is employed for feature selection. The proposed TSFS can be used for both supervised and unsupervised feature selection. This method is evaluated on different datasets and is compared with state-of-the-art existing feature selection methods. The results show that TSFS performs better in terms of classification and clustering accuracies and reconstruction error. Moreover, experimental evaluations demonstrate a low degree of sensitivity to parameter selection in the proposed method.