Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
404664 | Neural Networks | 2008 | 10 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jian-Wu Xu, Hovagim Bakardjian, Andrzej Cichocki, Jose C. Principe,