Complex-Valued vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data
Abstract
The contributions of this paper are twofold. First, we show the potential interest of Complex-Valued Neural Network (CVNN) on classification tasks for complex-valued datasets. To highlight this assertion, we investigate an example of complex-valued data in which the real and imaginary parts are statistically dependent through the property of non-circularity. In this context, the performance of fully connected feed-forward CVNNs is compared against a real-valued equivalent model. The results show that CVNN performs better for a wide variety of architectures and data structures. CVNN accuracy presents a statistically higher mean and median and lower variance than Real-Valued Neural Network (RVNN). Furthermore, if no regularization technique is used, CVNN exhibits lower overfitting. The second contribution is the release of a Python library (Barrachina 2019) using Tensorflow as back-end that enables the implementation and training of CVNNs in the hopes of motivating further research on this area.
Cite
@article{arxiv.2009.08340,
title = {Complex-Valued vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data},
author = {Jose Agustin Barrachina and Chenfang Ren and Christele Morisseau and Gilles Vieillard and Jean-Philippe Ovarlez},
journal= {arXiv preprint arXiv:2009.08340},
year = {2021}
}
Comments
6 pages, 5 figures, conference, preprint