Orthogonal approach to independent component analysis using quaternionic factorization

Quaternionic Independent Component Analysis using hypercomplex nonlinearities

We propose a quaternionic version of the Infomax algorithm to perform ICA on quaternion valued data. We introduce the three possible types of nonlinearities that can be used as activation functions and derive their differentiability properties. It is shown that only hypercomplex (fully quaternionic) nonlinearity can lead to the estimation of all possible classes of proper quaternion random vari...

A semiparametric approach to source separation using independent component analysis

Data processing and source identification using lower dimensional hidden structure plays an essential role in many fields of applications, including image processing, neural networks, genome studies, signal processing and other areas where large datasets are often encountered. One of the common methods for source separation using lower dimensional structure involves the use of Independent Compo...

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

​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...