# Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization

arXiv: https://arxiv.org/abs/2010.13631 · HTML: https://mlsanity.com/paper/2010.13631/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [GANDALF: Gated Adaptive Network for Deep Automated Learning of Features](https://mlsanity.com/paper/2207.08548/) (2022)
- [Sequential Attention for Feature Selection](https://mlsanity.com/paper/2209.14881/) (2022)
- [VisageSynTalk: Unseen Speaker Video-to-Speech Synthesis via Speech-Visage Feature Selection](https://mlsanity.com/paper/2206.07458/) (2022)
- [Self-Learning Tuning for Post-Silicon Validation](https://mlsanity.com/paper/2111.08995/) (2021)
- [A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning](https://mlsanity.com/paper/2207.00336/) (2022)
- [Anomaly Detection Based on Selection and Weighting in Latent Space](https://mlsanity.com/paper/2103.04662/) (2021)
- [Conditional Variable Selection for Intelligent Test](https://mlsanity.com/paper/2207.00335/) (2022)
- [Deep Feature Selection Using a Novel Complementary Feature Mask](https://mlsanity.com/paper/2209.12282/) (2022)
- [SAND: One-Shot Feature Selection with Additive Noise Distortion](https://mlsanity.com/paper/2505.03923/) (2025)
- [Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning](https://mlsanity.com/paper/2209.15249/) (2022)
- [Performance of ℓ1 Regularization for Sparse Convex Optimization](https://mlsanity.com/paper/2307.07405/) (2023)

## Abstract (arXiv)

Feature selection is generally used as one of the most important preprocessing techniques in machine learning, as it helps to reduce the dimensionality of data and assists researchers and practitioners in understanding data. Thereby, by utilizing feature selection, better performance and reduced computational consumption, memory complexity and even data amount can be expected. Although there exist approaches leveraging the power of deep neural networks to carry out feature selection, many of them often suffer from sensitive hyperparameters. This paper proposes a feature mask module (FM-module) for feature selection based on a novel batch-wise attenuation and feature mask normalization. The proposed method is almost free from hyperparameters and can be easily integrated into common neural networks as an embedded feature selection method. Experiments on popular image, text and speech datasets have shown that our approach is easy to use and has superior performance in comparison with other state-of-the-art deep-learning-based feature selection methods.