[This is probably the most widely used algorithm for performing independent component analysis, a recently developed variant of factor analysis that is. Independent Component Analysis [Aapo Hyvärinen, Juha Karhunen, Erkki Oja] on *FREE* shipping on qualifying offers. A comprehensive. Aapo Hyvärinen and Erkki Oja. Helsinki University of with the title “Independent Component Analysis: Algorithms and Applications”. April 1 Motivation.

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In most of the widely used ICA algorithms, the non-quadratic functions G i are fixed; possibly just their signs are adapted, as is implicitly done in FastICA [ 77 ].

For example, an electrode placed on the scalp as in electroencephalography measures a weighted sum of the electrical activities of many brain areas. If we assume the data are Gaussian, the two models give equally good fits.

ICA is an unsupervised method in the sense that it takes the input data in the form of a single data matrix. National Center for Biotechnology InformationU. It has been realized that non-Gaussianity is in fact quite widespread in any applications dealing with scientific measurement devices as opposed to, for example, data in the social and human sciences. From the viewpoint of optimizing the statistical performance of the algorithm, it should be advantageous to learn estimate the optimal functions G i.

Different extensions of the basic framework consider temporal structure [ 19 ], and three-way structure [ 2021 ]. Scale mixing of symmetric distributions with zero means. This is because statistical independence is a very strong property with potentially an infinite number of degrees of freedom.

Series A, Mathematical, physical, and engineering sciences are provided here courtesy of Cojponent Royal Society. Neurocomputing 81— Dodge Y, Rousson V. Under these three conditions, the model is essentially identifiable [ 56 ]. Different computational strategies are available to cope with this problem, as reviewed by Calhoun et hyvaeinen.


An additional difficulty for such assessment in the case of ICA is the permutation indeterminacy: Paatero P, Tapper U. Open Access article [Extends the theory of the preceding paper to testing the values of the independent components themselves.

Typically, the literature uses the formalism where the index t is dropped, and the x i and the s i are considered random variables. In the basic theory, it is in fact assumed that the observations are independent and identically distribution i. It is hyvarihen kind of a combination of independent component analysis and wavelet shrinkage ideas.

The dependencies of the estimated “independent” components are visualized as a topographic order. Numerical optimization in the space of orthogonal matrices tends to be faster and more stable than in the general space of matrices, which is probably the main reason for making inddependent transformation. The generality and potential usefulness of the model were never in question, but in the early days of ICA, there was some doubt about the adequacy of the assumptions of non-Gaussianity and independence.

The formulation in 2. In the earliest work, the v i were divided into groups or subspaces ana,ysis that the variables hyvarnien the same group are positively correlated, while the variables in different groups are independent [ 39 ].

Signal Processing84 2: Joint estimation of linear non-Gaussian acyclic models. To validate ICA results, it might seem, at first sight, to be interesting to test the independence of the components because this is an important assumption in the model.

Advances in neural information processing systems 21Cambridge, MA: Articles from Philosophical transactions. In other words, their joint density anwlysis is factorizable: This is in stark contrast to uncorrelatedness, which means that 6.


It can give useful information on the interactions between the components or sources recovered by ICA. Estimating functions for blind separation when sources have variance dependencies.

Independent Component Analysis: A Tutorial

Basic theory of independent component analysis In this section, we provide a succinct exposition of the basic theory of ICA indepedent going to recent developments in subsequent sections. A closely related formalism uses a generative model of the whole covariance structure of x [ 4445 ]. In fact, linear temporal filtering does not change the validity of the linear mixing model, nor does it change the mixing matrix.

Pairwise measures of causal direction in linear non-gaussian acyclic models. Nonnegative matrix and tensor factorizations: The main breakthrough in the theory of ICA was the realization that the model can be made identifiable by making the unconventional assumption of the non-Gaussianity componebt the independent components [ 5 ].

Independent component analysis: recent advances

An adaptive method for subband decomposition ICA. Kernel independent component analysis. To assess computational reliability, we could run the ICA algorithm from many different initial points. Using this idea of analysing different datasets, it is actually possible to formulate a proper statistical testing hyvarimen, based on a null hypothesis, which gives p -values for each component.

NeuroImage 22— Thus, selecting the direction of causality is simply reduced to choosing between two ICA models. In those early models, the dependency structure of the v i is fixed a priori but see the extension by Gruber et al.

Blind separation of sources that have spatiotemporal variance dependencies. Choose between the following two models:.