Send to

Choose Destination
See comment in PubMed Commons below
Philos Trans A Math Phys Eng Sci. 2012 Dec 31;371(1984):20110534. doi: 10.1098/rsta.2011.0534. Print 2013 Feb 13.

Independent component analysis: recent advances.

Author information

Department of Computer Science, and HIIT, University of Helsinki, Helsinki, Finland.


Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model.

PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Support Center