Feature Selection Network (FsNet) is a scalable concrete neural network architecture for Wide data. Wide data consists of high-dimensional and small number of samples. Specifically, FsNet consists of a selector layer that uses a concrete random variable for discrete feature selection and a supervised deep neural network regularized with the reconstruction loss. Because a large number of parameters in the selector and reconstruction layer can easily cause overfitting under a limited number of samples, we use two tiny networks to predict the large virtual weight matrices of the selector and reconstruction layers.
For more details, see the accompanying paper: "FsNet: Feature Selection Network on High-dimensional Biological Data", arXive, and please use the citation below.
@article{singh2020fsnet,
title={FsNet: Feature Selection Network on High-dimensional Biological Data},
author={Dinesh Singh and Makoto Yamada},
journal={arXiv preprint arXiv:2001.08322},
year={2020}
}