Convolutional Neural Network Feature Reduction using Wavelet Transform

Authors

  • A. Levinskis Riga Technical University

DOI:

https://doi.org/10.5755/j01.eee.19.3.3698

Keywords:

Wavelet transform, artificial, neural networks, frequency estimation

Abstract

Paper describes wavelet transform possible application for convolutional neural networks (CNN). As it already known, wavelet transform gives good signal representation in time and frequency domains. This can be useful for CNN input feature reduction as well as architecture simplicity by using only part of coefficients.  The result of work is set of experiment which enables to configure out the most appropriate coefficient part. After feature reductions and architecture simplicity achieved configuration could classify data almost ten times faster than original.

DOI: http://dx.doi.org/10.5755/j01.eee.19.3.3698

Downloads

Published

2013-03-07

How to Cite

Levinskis, A. (2013). Convolutional Neural Network Feature Reduction using Wavelet Transform. Elektronika Ir Elektrotechnika, 19(3), 61-64. https://doi.org/10.5755/j01.eee.19.3.3698

Issue

Vol. 19 No. 3 (2013)

Section

SIGNAL TECHNOLOGY

License

The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.

By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.