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Independent component analysis: algorithms and applications
TLDR
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. Expand
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Independent Component Analysis
TLDR
Independent Component Analysis (ICA) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. Expand
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Fast and robust fixed-point algorithms for independent component analysis
  • A. Hyvärinen
  • Mathematics, Computer Science
  • IEEE Trans. Neural Networks
  • 1 May 1999
TLDR
We introduce a family of new contrast (objective) functions for ICA using maximum entropy approximations of differential entropy, which enable both the estimation of the whole decomposition by minimizing mutual information and estimation of individual independent components as projection pursuit directions. Expand
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Independent Component Analysis
In this chapter, we discuss a statistical generative model called independent component analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse coding. It showsExpand
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
TLDR
We present a new estimation principle for parameterized statistical models, i.e. models where the density function does not integrate to one. Expand
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Survey on Independent Component Analysis
TLDR
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 that minimizes the statistical dependence of the components of the representation. Expand
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Validating the independent components of neuroimaging time series via clustering and visualization
TLDR
We present a method for assessing both the algorithmic and statistical reliability of estimated independent components of an ICA algorithm. Expand
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A Linear Non-Gaussian Acyclic Model for Causal Discovery
TLDR
We show how to discover the causal structure of continuous-valued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-Gaussian distributions of non-zero variances. Expand
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A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
TLDR
A fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals and its computational efficiency is shown by simulations. Expand
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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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