A. Hyvärinen

Publications327
h-index64
Citations42,215
Highly Influential Citations4,573
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Independent component analysis: algorithms and applications
Fast and robust fixed-point algorithms for independent component analysis
  • A. Hyvärinen
  • Computer Science, Mathematics
    IEEE Trans. Neural Networks
  • 1 May 1999
TLDR
Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Independent Component Analysis
TLDR
Although ICA was originally developed for digital signal processing applications, it has recently been found that it may be a powerful tool for analyzing text document data as well, if the documents are presented in a suitable numerical form.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
TLDR
A new estimation principle is presented to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity, which leads to a consistent (convergent) estimator of the parameters.
Independent Component Analysis
TLDR
A statistical generative model called independent component analysis is discussed, which shows how sparse coding can be interpreted as providing a Bayesian prior, and answers some questions which were not properly answered in the sparse coding framework.
A Linear Non-Gaussian Acyclic Model for Causal Discovery
TLDR
This work shows how to discover the complete 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.
Survey on Independent Component Analysis
TLDR
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.
Validating the independent components of neuroimaging time series via clustering and visualization
Estimation of Non-Normalized Statistical Models by Score Matching
  • A. Hyvärinen
  • Computer Science
    J. Mach. Learn. Res.
  • 1 December 2005
TLDR
While the estimation of the gradient of log-density function is, in principle, a very difficult non-parametric problem, it is proved a surprising result that gives a simple formula that simplifies to a sample average of a sum of some derivatives of the log- density given by the model.
A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
TLDR
In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations and the local consistency of the estimator given by the algorithm is proved.
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