A new nonlinear similarity measure for multichannel signals

We propose a novel similarity measure, called the correntropy coefficient, sensitive to higher order moments of the signal statistics based on a similarity function called the cross-correntopy. Cross-correntropy nonlinearly maps the original time series into a high-dimensional reproducing kernel Hilbert space (RKHS). The correntropy coefficient computes the cosine of the angle between the transformed vectors. Preliminary experiments with simulated data and multichannel electroencephalogram (EEG) signals during behaviour studies elucidate the performance of the new measure versus the well-established correlation coefficient. c 2008 Elsevier Ltd. All rights reserved.

• Type: Journal
• Year: 2008
• Where: NN
• Authors: Jian-Wu Xu, Hovagim Bakardjian, Andrzej Cichocki, Jose C. Principe
• Keywords: Correlation Coefficient, Neural Networks, NN 2008, Original Time Series, Reproducing Kernel Hilbert Space