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.
View on arXiv@article{larrubia2025_2506.12039,
title={ The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks },
author={ Leonardo Fonseca Larrubia and Pedro Alberto Morettin and Chang Chiann },
journal={arXiv preprint arXiv:2506.12039},
year={ 2025 }
}