# The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks

arXiv: https://arxiv.org/abs/2506.12039 · HTML: https://mlsanity.com/paper/2506.12039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 12 of 12)

- [Understanding Deep Convolutional Networks](https://mlsanity.com/paper/1601.04920/) (2016)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Kymatio: Scattering Transforms in Python](https://mlsanity.com/paper/1812.11214/) (2018)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Deep Roto-Translation Scattering for Object Classification](https://mlsanity.com/paper/1412.8659/) (2014)
- [Scaling the Scattering Transform: Deep Hybrid Networks](https://mlsanity.com/paper/1703.08961/) (2017)
- [Scattering Networks for Hybrid Representation Learning](https://mlsanity.com/paper/1809.06367/) (2018)
- [Deep Network Classification by Scattering and Homotopy Dictionary Learning](https://mlsanity.com/paper/1910.03561/) (2019)
- [Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties](https://mlsanity.com/paper/1805.00571/) (2018)
- [Wavelet Scattering Regression of Quantum Chemical Energies](https://mlsanity.com/paper/1605.04654/) (2016)
- [Deep Haar Scattering Networks](https://mlsanity.com/paper/1509.09187/) (2015)
- [Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement](https://mlsanity.com/paper/2011.11586/) (2020)

## Built on by (most cited first, 1 of 1)

- [Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals](https://mlsanity.com/paper/2605.14014/) (2026)

## Abstract (arXiv)

We present the Maximal Overlap Discrete Wavelet Scattering Transform (MODWST), whose construction is inspired by the combination of the Maximal Overlap Discrete Wavelet Transform (MODWT) and the Scattering Wavelet Transform (WST). We also discuss the use of MODWST in classification tasks, evaluating its performance in two applications: stationary signal classification and ECG signal classification. The results demonstrate that MODWST achieved good performance in both applications, positioning itself as a viable alternative to popular methods like Convolutional Neural Networks (CNNs), particularly when the training data set is limited.