All-sky astrophysical component separation with Fast Independent Component Analysis (FASTICA)

A FastICA Algorithm for Non-negative Independent Component Analysis

The non-negative ICA problem is here defined by the constraint that the sources are non-negative with probability one. This case occurs in many practical applications like spectral or image analysis. It has then been shown by [10] that there is a straightforward way to find the sources: if one whitens the non-zero-mean observations and makes a rotation to positive factors, then these must be th...

All - sky component separation for the Planck mission

An harmonic-space maximum-entropy method (MEM) is presented for separating the emission from different physical components in all-sky observations by the forthcoming Planck satellite. The analysis is performed at full Planck resolution, with a pixel size of 1.7 arcmin, which corresponds to lmax ≈ 6000. The simulated Planck data include emission from the CMB, the kinetic and thermal Sunyaev-Zel’...

An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implemen...

​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...

Independent Component Analysis and Blind Source Separation