An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

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...

An alternative approach to infomax and independent component analysis

Independent component analysis: an introduction.

Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides a...

Independent Component Analysis: An Introduction

Independent Component Analysis (ICA) can be described in several ways, one of which is as a technique that seeks to find a set directions (components) underlying multivariate data that are most independent of one another. While there are several ICA models and many ICA methods, in this report we focus on the most basic model and one of the most popular and simple algorithms; the One-Unit FastIC...

An Introduction to Independent Component Analysis and Blind Source Separation

1 ICA 2 1.1 Examples of linear mixtures of independent components . . . . . 2 1.2 Basic assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 ICA from Maximum Likelihood . . . . . . . . . . . . . . . . . . . 4 1.4 PCA and ICA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Minimal Mutual Information . . . . . . . . . . . . . . . . . . . . 6 1.6 Maximum Transmitte...