Title :
A parameter-free kernel design based on cumulative distribution function for correntropy
Author :
Jongmin Lee ; Pingping Zhu ; Principe, Jose C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
Abstract :
This paper proposes a parameter-free kernel that is translation invariant and positive definite. The new kernel is based on the data cumulative distribution function (CDF) that provides all the statistical information about the observed samples. Without an explicit kernel size parameter, this novel kernel is used to define the autocorrentropy function, which is a generalized similarity measure, and spectral density estimator. Numerical examples show that the proposed method provides comparable performance to the existing Gaussian kernel with optimized kernel size.
Keywords :
entropy; functions; statistics; CDF; Gaussian kernel; autocorrentropy function; cumulative distribution function; generalized similarity measure; parameter-free kernel design; positive definite kernel; spectral density estimator; translation invariant kernel; Correlation; Estimation; Fourier transforms; Kernel; Noise; Random processes;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6707021