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Aapo Hyvärinen
Researcher at University of Helsinki
Publications - 301
Citations - 48801
Aapo Hyvärinen is an academic researcher from University of Helsinki. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 61, co-authored 301 publications receiving 44146 citations. Previous affiliations of Aapo Hyvärinen include Helsinki Institute for Information Technology & Helsinki University of Technology.
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Independent Component Analysis
TL;DR: Independent component analysis as mentioned in this paper is a statistical generative model based on sparse coding, which is basically a proper probabilistic formulation of the ideas underpinning sparse coding and can be interpreted as providing a Bayesian prior.
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Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
Journal ArticleDOI
Fast and robust fixed-point algorithms for independent component analysis
TL;DR: 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.
Journal ArticleDOI
A fast fixed-point algorithm for independent component analysis
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: A novel fast algorithm for independent component analysis is introduced, which can be used for blind source separation and feature extraction, and the convergence speed is shown to be cubic.
Proceedings Article
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
TL;DR: 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.