提出一种新型小波变换，适合小数据量信号分类
The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks
- 结合MODWT与WST构造新变换，保留时频信息
- 在两类信号分类中表现良好，小样本下优于CNN
- 适合数据少的场景，可替代深度学习模型
我们提出最大重叠离散小波散射变换（MODWST），其结构受到最大重叠离散小波变换（MODWT）与小波散射变换（WST）结合的启发。本文还探讨了MODWST在分类任务中的应用，评估其在两类任务中的表现：平稳信号分类与心电图（ECG）信号分类。结果表明，MODWST在两项任务中均表现出良好性能，尤其在训练数据有限的情况下，展现出可作为卷积神经网络（CNNs）等主流方法的可行替代方案。
原文摘要 · Abstract (English)
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.
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