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Independent component analysis: algorithms and applications
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computationalExpand
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Independent Component Analysis
Independent Component Analysis (ICA) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. ICA assumes a statistical model whereby the observedExpand
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Fast and robust fixed-point algorithms for independent component analysis
  • A. Hyvärinen
  • Medicine, Computer Science
  • IEEE Trans. Neural Networks
  • 1 May 1999
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other asExpand
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Independent Component Analysis
learning, psychological motivated conditioning, error-correcting algorithms etc.). While the book certainly has a coherent perspective, and contains many interesting details useful also forExpand
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Survey on Independent Component Analysis
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. ForExpand
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Validating the independent components of neuroimaging time series via clustering and visualization
Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; thatExpand
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificiallyExpand
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A Linear Non-Gaussian Acyclic Model for Causal Discovery
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data. Such methods make various assumptions on the data generating process toExpand
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A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valuedExpand
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A Fast Fixed-Point Algorithm for Independent Component Analysis
We introduce a novel fast algorithm for independent component analysis, which can be used for blind source separation and feature extraction. We show how a neural network learning rule can beExpand
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