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Independent Component Analysis

2021
Conventional EOFs yield orthogonal spatial patterns and uncorrelated time series. Non-correlation does not necessarily yield independence, which is a strong constraint compared to non-correlation. This chapter discusses the concept of independence, and its relation to non-normality and describes different ways to obtain independent components.
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Independent Component Analysis

IEEE Transactions on Neural Networks, 2004
Independent component analysis (ICA) is a multivariate data analysis method that, given a linear mixture of statistical independent sources, recovers these components by producing an unmixing matrix. Stemming from a more general problem called blind source separation (BSS), ICA has become increasingly popular in recent years with several excellent ...
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Independent Components Analysis

2000
Independent Components Analysis has recently become an important tool for modelling and understanding empirical datasets. In this chapter we review the theoretical basis of ICA, outline an approach to non-stationary ICA, and describe a number of biomedical case studies.
Richard Everson, Stephen J. Roberts
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Independent Component Analysis

2013
Imagine that you are attending a cocktail party, the surrounding is full of chatting and noise, and somebody is talking about you. In this case, your ears are particularly sensitive to this speaker. This is the cocktail-party problem, which can be solved by blind source separation (BSS).
Ke-Lin Du, M. N. S. Swamy
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Independent Component Analysis

2011
Independent Component Analysis(ICA) is one of the methods for solving blind source separation in blind signal processing. This method seeks for a linear coordinate system to produce signals that are mutually statistically independent. Compared with Principal Component Analysis (PCA) based on correlation transform, ICA decorrelates signals and reduces ...
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Independent Component Analysis

Independent component analysis (ICA) was originally introduced in the signal processing literature as a blind source separation method with the goal to recover mutual independent non-Gaussian components based on an observed vector alone. The problem was first introduced in the 1980s and formalized in Comon ( 1994). From a statistical point of view, the
Nordhausen Klaus, Taskinen Sara
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Independent component analysis: recent advances

Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2013
Aapo Hyvärinen
exaly  

Imaging human EEG dynamics using independent component analysis

Neuroscience and Biobehavioral Reviews, 2006
Marissa Westerfield, Jeanne Townsend, Scott Makeig   +2 more
exaly  

A new regression method based on independent component analysis

Talanta, 2006
Xueguang Shao, Zhenyu Hou, Wensheng Cai   +2 more
exaly  

Imaging brain dynamics using independent component analysis

Proceedings of the IEEE, 2001
Tzyy-Ping Jung, Scott Makeig, Martin J Mckeown   +2 more
exaly