Add to ORKGAbstract—The measure of similarity normally utilized in statistical signal processing is based on second order moments. In this paper, we reveal the probabilistic meaning of correntropy as a new localized similarity measure based on information theoretic learning (ITL) and kernel methods. As such it has vastly different properties when compared with Mean Square Error (MSE) that can be very useful in nonlinear, non-Gaussian signal processing. Two examples are presented to illustrate the technique
We propose a new approach for measuring similarity between two signals, which is applicable to many ...
International audienceFor images, stochastic resonance or useful-noise effects have previously been ...
Abstract—With an abundance of tools based on kernel methods and information theoretic learning, a vo...
Abstract—The optimality of second-order statistics depends heavily on the assumption of Gaussianity....
The self organizing map (SOM) is one of the popular clustering and data visualization algorithms and...
Abstract—Statistical tests have become an essential step in nonlinear system modeling due to the com...
Adopting a measure is essential in many multimedia applications. Recently, distance learning is beco...
To study concepts that are coded in language, researchers often collect lists of conceptual properti...
The use of Pearsonâ s correlation coefficient in Author Cocitation Analysis was compared with Salton...
The nonparametric Sample Entropy (SE) estimator has become a standard for the quantification of stru...
© 2015 Yunlong Feng, Xiaolin Huang, Lei Shi, Yuning Yang, and Johan A.K. Suykens. Within the statist...
Abstract. This paper addresses the issue of quantifying asymmetric functional relationships between ...
International audienceIn this paper, we propose an algorithm for learning a general class of similar...
A new technique for feature withdrawal by neural response is going to be familiarized in this resear...
We propose a similarity measure for comparing digital images. The technique is based on mutual info...
We propose a new approach for measuring similarity between two signals, which is applicable to many ...
International audienceFor images, stochastic resonance or useful-noise effects have previously been ...
Abstract—With an abundance of tools based on kernel methods and information theoretic learning, a vo...
Abstract—The optimality of second-order statistics depends heavily on the assumption of Gaussianity....
The self organizing map (SOM) is one of the popular clustering and data visualization algorithms and...
Abstract—Statistical tests have become an essential step in nonlinear system modeling due to the com...
Adopting a measure is essential in many multimedia applications. Recently, distance learning is beco...
To study concepts that are coded in language, researchers often collect lists of conceptual properti...
The use of Pearsonâ s correlation coefficient in Author Cocitation Analysis was compared with Salton...
The nonparametric Sample Entropy (SE) estimator has become a standard for the quantification of stru...
© 2015 Yunlong Feng, Xiaolin Huang, Lei Shi, Yuning Yang, and Johan A.K. Suykens. Within the statist...
Abstract. This paper addresses the issue of quantifying asymmetric functional relationships between ...
International audienceIn this paper, we propose an algorithm for learning a general class of similar...
A new technique for feature withdrawal by neural response is going to be familiarized in this resear...
We propose a similarity measure for comparing digital images. The technique is based on mutual info...
We propose a new approach for measuring similarity between two signals, which is applicable to many ...
International audienceFor images, stochastic resonance or useful-noise effects have previously been ...
Abstract—With an abundance of tools based on kernel methods and information theoretic learning, a vo...