Deep Haar Scattering Networks in Pattern Recognition: A promising approach
Abstract
The aim of this paper is to discuss the use of Haar scattering networks, which is a very simple architecture that naturally supports a large number of stacked layers, yet with very few parameters, in a relatively broad set of pattern recognition problems, including regression and classification tasks. This architecture, basically, consists of stacking convolutional filters, that can be thought as a generalization of Haar wavelets, followed by non-linear operators which aim to extract symmetries and invariances that are later fed in a classification/regression algorithm. We show that good results can be obtained with the proposed method for both kind of tasks. We have outperformed the best available algorithms in 4 out of 18 important data classification problems, and have obtained a more robust performance than ARIMA and ETS time series methods in regression problems for data with strong periodicities.
Keywords
Cite
@article{arxiv.1811.12081,
title = {Deep Haar Scattering Networks in Pattern Recognition: A promising approach},
author = {Fernando Fernandes Neto and Alemayehu Admasu Solomon and Rodrigo de Losso and Claudio Garcia and Pedro Delano Cavalcanti},
journal= {arXiv preprint arXiv:1811.12081},
year = {2018}
}