PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Independent component analysis and beyond
Stefan Harmeling
(2004) PhD thesis, Universität Potsdam.

Abstract

Independent component analysis (ICA) is a tool for statistical data analysis and signal processing that is able to decompose multivariate signals into their underlying source components. Although the classical ICA model is highly useful, there are many real-world applications that require powerful extensions of ICA. This thesis presents new methods that extend the functionality of ICA: (1) reliability and grouping of independent components with noise injection, (2) robust and overcomplete ICA with inlier detection, and (3) nonlinear ICA with kernel methods.

EPrint Type:Thesis (PhD)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1904
Deposited By:Stefan Harmeling
Deposited On:29 December 2005