Efficient independent component analysis

Efficient independent component analysis

Independent component analysis (ICA) has been widely used for blind source separation in many fields, such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on Mestimates have been proposed for estimating the mixing matrix. Recently, several nonparametric methods have been developed, but in-depth analysis of asymptotic efficiency has not been ...

​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...

Efficient source adaptivity in independent component analysis

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "true" score functions of the sources. We describe an efficient scheme for achieving this by exten...

Flexible and efficient implementations of Bayesian independent component analysis

In this paper we present an empirical Bayes method for flexible and efficient Independent Component Analysis (ICA). The method is flexible with respect to choice of source prior, dimensionality and positivity of the mixing matrix, and structure of the noise covariance matrix. The efficiency is ensured using parameter optimizers which are more advanced than the expectation maximization (EM) algo...

Vlassis and Motomura : Efficient Source Adaptivity in Independent Component Analysis