Proposal of a Methodology Based on Using a Wavelet Transform as a Convolution Operation in a Convolutional Neural Network for Feature Extraction Purposes

Using methodological tools to construct feature extraction from multidimensional data is challenging. Different treatments are required to build a coherent representation with those features that can be attenuated by various phenomena inherent to the observed process. It is interesting to note that in this methodological generation, several methods converge, such as Wavelet transform, focusing on convolution processing, windowed data shifting, and classification via Self-Organizing Maps. Likewise, a case study is presented in this work, allowing us to understand the scope of this methodological tool using an information cube to detect common features, as discussed previously.