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arXiv · 2009.08340

Complex-Valued vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data

Jose Agustin Barrachina·Chenfang Ren·Christele Morisseau·Gilles Vieillard·Jean-Philippe Ovarlez
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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.

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Jose Agustin Barrachina, Chenfang Ren, Christele Morisseau, Gilles Vieillard, Jean-Philippe Ovarlez. 2020-09-17. Complex-Valued vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data. https://arxiv.org/abs/2009.08340

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