Topic
Independent component analysis
About: Independent component analysis is a research topic. Over the lifetime, 9169 publications have been published within this topic receiving 241456 citations.
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TL;DR: FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components, and is being used in the default rfMRI processing pipeline for generating HCP connectomes.
1,565 citations
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TL;DR: Simulations demonstrate that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data.
1,465 citations
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TL;DR: This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization and compares the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.
Abstract: This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.
1,271 citations
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TL;DR: It is shown that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency and provide a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.
Abstract: In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in matching structure in the data. Overcomplete codes have also been proposed as a model of some of the response properties of neurons in primary visual cortex. Previous work has focused on finding the best representation of a signal using a fixed overcomplete basis (or dictionary). We present an algorithm for learning an overcomplete basis by viewing it as probabilistic model of the observed data. We show that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency. This can be viewed as a generalization of the technique of independent component analysis and provides a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.
1,267 citations
01 Jan 1999
TL;DR: This paper surveys the existing theory and methods for independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation.
Abstract: A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA.
1,231 citations