Independent component analysis: algorithms and applications
- Aapo Hyvärinen, E. Oja
- Computer ScienceNeural Networks
- 1 May 2000
Fast and robust fixed-point algorithms for independent component analysis
- Aapo Hyvärinen
- Computer Science, MathematicsIEEE Trans. Neural Networks
- 1 May 1999
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
- Aapo Hyvärinen, J. Karhunen, E. Oja
- PsychologyIEEE Transactions on Neural Networks
- 18 May 2001
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 shows…
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
- Michael U Gutmann, Aapo Hyvärinen
- Computer ScienceInternational Conference on Artificial…
- 31 March 2010
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.
A Linear Non-Gaussian Acyclic Model for Causal Discovery
- Shohei Shimizu, P. Hoyer, Aapo Hyvärinen, Antti J. Kerminen
- Computer ScienceJournal of machine learning research
- 1 December 2006
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
- Aapo Hyvärinen
- Mathematics
- 1999
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.
Estimation of Non-Normalized Statistical Models by Score Matching
- Aapo Hyvärinen
- Computer ScienceJournal of machine learning research
- 1 December 2005
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.
Validating the independent components of neuroimaging time series via clustering and visualization
- J. Himberg, Aapo Hyvärinen, F. Esposito
- Computer ScienceNeuroImage
- 1 July 2004
A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
- E. Bingham, Aapo Hyvärinen
- Computer ScienceInternational Journal of Neural Systems
- 1 February 2000
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.
A Fast Fixed-Point Algorithm for Independent Component Analysis
- Aapo Hyvärinen, E. Oja
- Computer ScienceNeural Computation
- 1 October 1997
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.
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