Performance comparison of new nonparametric independent component analysis algorithm for different entropic indexes
نویسندگان
چکیده
Most independent component analysis (ICA) algorithms use mutual information (MI) measures based on Shannon entropy as a cost function, but Shannon entropy is not the only measure in the literature. In this paper, instead of Shannon entropy, Tsallis entropy is used and a novel ICA algorithm, which uses kernel density estimation (KDE) for estimation of source distributions, is proposed. KDE is directly evaluated from the original data samples, so it solves the important problem in ICA: how to choose nonlinear functions as the probability density function (pdf) estimation of the sources.
منابع مشابه
Rank based Least-squares Independent Component Analysis
In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...
متن کاملFeature selection using genetic algorithm for classification of schizophrenia using fMRI data
In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component analysis (ICA) is used for further data analysis. It estimates independent components (ICs) of...
متن کاملA Survey of The Performance Indexes of ICA Algorithms
This paper deals with the problem of blind separation of sources (BSS). In the literature, one can find many Independent Component Algorithms (ICA) to solve the BSS. To demonstrate the performances of their algorithms, researchers often use different methods or performance indexes depending on their source signals and their applications. Many methods and performance indexes can not be used to c...
متن کاملRepresenting Spectral data using LabPQR color space in comparison to PCA method
In many applications of color technology such as spectral color reproduction it is of interest to represent the spectral data with lower dimensions than spectral space’s dimensions. It is more than half of a century that Principal Component Analysis PCA method has been applied to find the number of independent basis vectors of spectral dataset and representing spectral reflectance with lower di...
متن کاملA New Nonparametric Regression for Longitudinal Data
In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus ...
متن کامل