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

Jose Agustin Barrachina; Chenfang Ren; Christèle Morisseau; Gilles Vieillard; Jean Philippe Ovarlez

ICASSP 2021accepted0 citations

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

Jose Agustin Barrachina, Chenfang Ren, Christèle Morisseau, Gilles Vieillard, Jean Philippe Ovarlez

Abstract

This paper shows the benefits of using Complex-Valued Neural Network (CVNN) on classification tasks for non-circular complex-valued datasets. Motivated by radar and especially Synthetic Aperture Radar (SAR) applications, we propose a statistical analysis of fully connected feed-forward neural networks performance in the cases where real and imaginary parts of the data are correlated through the non-circular property. In this context, comparisons between CVNNs and their real-valued equivalent models are conducted, showing that CVNNs provide better performance for multiple types of non-circularity. Notably, CVNNs statistically perform less overfitting, higher accuracy and provide shorter confidence intervals than its equivalent Real-Valued Neural Network (RVNN).

BibTeX

@inproceedings{icassp2021_complexvaluedvsr,
  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 Christèle Morisseau and Gilles Vieillard and Jean Philippe Ovarlez},
  booktitle = {ICASSP 2021},
  year = {2021}
}