| Paper ID | MLSP-9.6 |
| Paper Title |
COMPLEX-VALUED VS. REAL-VALUED NEURAL NETWORKS FOR CLASSIFICATION PERSPECTIVES: AN EXAMPLE ON NON-CIRCULAR DATA |
| Authors |
Jose Agustin Barrachina, Chengfang Ren, ONERA/CentraleSupelec, France; Christele Morisseau, Gilles Vieillard, ONERA, France; Jean-Philippe Ovarlez, ONERA/CentraleSupelec, France |
| Session | MLSP-9: Learning Theory for Neural Networks |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-LEAR] Learning theory and algorithms |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 Networks (RVNN). |
