# FsNet: Feature Selection Network on High-dimensional Biological Data

arXiv: https://arxiv.org/abs/2001.08322 · HTML: https://mlsanity.com/paper/2001.08322/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 23 of 23)

- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Where to Pay Attention in Sparse Training for Feature Selection?](https://mlsanity.com/paper/2211.14627/) (2022)
- [End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax](https://mlsanity.com/paper/2102.09050/) (2021)
- [Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks](https://mlsanity.com/paper/2303.07200/) (2023)
- [Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks](https://mlsanity.com/paper/2204.01682/) (2022)
- [GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data](https://mlsanity.com/paper/2211.06302/) (2022)
- [Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data](https://mlsanity.com/paper/2211.15616/) (2022)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)
- [Unveiling the Power of Sparse Neural Networks for Feature Selection](https://mlsanity.com/paper/2408.04583/) (2024)
- [How good Neural Networks interpretation methods really are? A quantitative benchmark](https://mlsanity.com/paper/2304.02383/) (2023)
- [SLM: End-to-end Feature Selection via Sparse Learnable Masks](https://mlsanity.com/paper/2304.03202/) (2023)
- [Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs](https://mlsanity.com/paper/2306.15661/) (2023)
- [A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface](https://mlsanity.com/paper/2402.10641/) (2024)
- [Conditional Gumbel-Softmax for constrained feature selection with application to node selection in wireless sensor networks](https://mlsanity.com/paper/2406.01162/) (2024)
- [Top-k Regularization for Supervised Feature Selection](https://mlsanity.com/paper/2106.02197/) (2021)
- [Few-shot Learning for Unsupervised Feature Selection](https://mlsanity.com/paper/2107.00816/) (2021)
- [Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection](https://mlsanity.com/paper/2201.12910/) (2022)
- [Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fields](https://mlsanity.com/paper/2206.13968/) (2022)
- [Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder](https://mlsanity.com/paper/2306.04795/) (2023)
- [Sparse Linear Centroid-Encoder: A Convex Method for Feature Selection](https://mlsanity.com/paper/2306.04824/) (2023)
- [Deep Learning for Efficient GWAS Feature Selection](https://mlsanity.com/paper/2312.15055/) (2023)
- [Genetic Programming for Explainable Manifold Learning](https://mlsanity.com/paper/2403.14139/) (2024)
- [A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets](https://mlsanity.com/paper/2405.02534/) (2024)

## Abstract (arXiv)

Biological data including gene expression data are generally high-dimensional and require efficient, generalizable, and scalable machine-learning methods to discover their complex nonlinear patterns. The recent advances in machine learning can be attributed to deep neural networks (DNNs), which excel in various tasks in terms of computer vision and natural language processing. However, standard DNNs are not appropriate for high-dimensional datasets generated in biology because they have many parameters, which in turn require many samples. In this paper, we propose a DNN-based, nonlinear feature selection method, called the feature selection network (FsNet), for high-dimensional and small number of sample data. Specifically, FsNet comprises a selection layer that selects features and a reconstruction layer that stabilizes the training. Because a large number of parameters in the selection and reconstruction layers can easily result in overfitting under a limited number of samples, we use two tiny networks to predict the large, virtual weight matrices of the selection and reconstruction layers. Experimental results on several real-world, high-dimensional biological datasets demonstrate the efficacy of the proposed method.