Results 261 to 270 of about 28,681,192 (295)

Independent component analysis: an introduction

Trends in Cognitive Sciences, 2002
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.
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Applications of Independent Component Analysis

2004
Blind source separation (BSS) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. The most basic statistical approach to BSS is Independent Component Analysis (ICA). It assumes a statistical model whereby the observed multivariate data are assumed to be linear or nonlinear mixtures of some unknown ...
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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 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

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 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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Blood Component Therapy for Cancer

Ca-A Cancer Journal for Clinicians, 1977
Benjamin Lichtiger, B Lichtiger
exaly  

Medullary Thyroid Carcinoma: A Component of an Interesting Endocrine Syndrome

Ca-A Cancer Journal for Clinicians, 1969
Melvin A Block
exaly