The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks
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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.
Key facts. Cited by 1 paper 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
12 of 24 references.
References · 12
- 2016Understanding Deep Convolutional Networkscited by 76
- 2018ECG Heartbeat Classification: A Deep Transferable Representationcited by 76
- 2018Kymatio: Scattering Transforms in Pythoncited by 58
- 2013Deep Scattering Spectrumcited by 52
- 2014Deep Roto-Translation Scattering for Object Classificationcited by 50
- 2017Scaling the Scattering Transform: Deep Hybrid Networkscited by 35
- 2018Scattering Networks for Hybrid Representation Learningcited by 33
- 2019Deep Network Classification by Scattering and Homotopy Dictionary Learningcited by 17
- 2018Solid Harmonic Wavelet Scattering for Predictions of Molecule Propertiescited by 12
- 2016Wavelet Scattering Regression of Quantum Chemical Energiescited by 8
- 2015Deep Haar Scattering Networkscited by 5
- 2020Scattering Transform Based Image Clustering using Projection onto Orthogonal Complementcited by 3
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Citing papers per year: 2026: 1.
1 paper.