# Explainable and Class-Revealing Signal Feature Extraction via Scattering Transform and Constrained Zeroth-Order Optimization

arXiv: https://arxiv.org/abs/2502.05722 · HTML: https://mlsanity.com/paper/2502.05722/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning](https://mlsanity.com/paper/2006.06224/) (2020)
- [Kymatio: Scattering Transforms in Python](https://mlsanity.com/paper/1812.11214/) (2018)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [A consensus-based global optimization method for high dimensional machine learning problems](https://mlsanity.com/paper/1909.09249/) (2019)
- [Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning](https://mlsanity.com/paper/2001.11988/) (2020)
- [On the Global Convergence of Particle Swarm Optimization Methods](https://mlsanity.com/paper/2201.12460/) (2022)
- [A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex Stochastic Optimization](https://mlsanity.com/paper/2205.01633/) (2022)
- [Underwater object classification using scattering transform of sonar signals](https://mlsanity.com/paper/1707.03133/) (2017)
- [Monogenic Wavelet Scattering Network for Texture Image Classification](https://mlsanity.com/paper/2202.12491/) (2022)
- [The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification](https://mlsanity.com/paper/2206.07857/) (2022)

## Abstract (arXiv)

We propose a new method to extract discriminant and explainable features from a particular machine learning model, i.e., a combination of the scattering transform and the multiclass logistic regression. Although this model is well-known for its ability to learn various signal classes with high classification rate, it remains elusive to understand why it can generate such successful classification, mainly due to the nonlinearity of the scattering transform. In order to uncover the meaning of the scattering transform coefficients selected by the multiclass logistic regression (with the Lasso penalty), we adopt zeroth-order optimization algorithms to search an input pattern that maximizes the class probability of a class of interest given the learned model. In order to do so, it turns out that imposing sparsity and smoothness of input patterns is important. We demonstrate the effectiveness of our proposed method using a couple of synthetic time-series classification problems.