2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-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
SessionMLSP-9: Learning Theory for Neural Networks
LocationGather.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
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).