Spectral Synthesis via Mean Field approach to Independent Component Analysis

Spectral independent component analysis

Independent component analysis (ICA) of a mixed signal into a linear combination of its independent components, is one of the main problems in statistics, with wide range of applications. The un-mixing is usually performed by finding a rotation that optimizes a functional closely related to the differential entropy. In this paper we solve the linear ICA problem by analyzing the spectrum and eig...

A Novel Approach to Independent Component Analysis

Independent component analysis (ICA) is a computational method, based on neural learning algorithm, to separate source signals from the observed mixtures by assuming that the sources are non-Gaussian in nature. Convergence speed, Area and Power are important parameters to be improved in VLSI implementation of ICA techniques, since they involve large number of iterative calculations, area and po...

Independent Component Analysis: An Approach to Clustering

A New Approach to Near-Infrared Spectral Data Analysis Using Independent Component Analysis

This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based met...

​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...