A. Hyvärinen
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- Publications
- Influence
Independent component analysis: algorithms and applications
- A. Hyvärinen, E. Oja
- Mathematics, Computer Science
- Neural Networks
- 1 May 2000
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 computational… Expand
Independent Component Analysis
- A. Hyvärinen, J. Karhunen, E. Oja
- Computer Science
- 2001
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 observed… Expand
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 as… Expand
Independent Component Analysis
- A. Hyvärinen
- IEEE Transactions on Neural Networks
- 2004
learning, psychological motivated conditioning, error-correcting algorithms etc.). While the book certainly has a coherent perspective, and contains many interesting details useful also for… Expand
Survey on Independent Component Analysis
- A. Hyvärinen
- Computer Science
- 1999
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. For… Expand
Validating the independent components of neuroimaging time series via clustering and visualization
- J. Himberg, A. Hyvärinen, F. Esposito
- Computer Science, Medicine
- NeuroImage
- 1 July 2004
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; that… Expand
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
- M. Gutmann, A. Hyvärinen
- Mathematics, Computer Science
- AISTATS
- 31 March 2010
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 artificially… Expand
A Linear Non-Gaussian Acyclic Model for Causal Discovery
- Shohei Shimizu, P. O. Hoyer, A. Hyvärinen, Antti J. Kerminen
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 1 December 2006
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 to… Expand
A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
- E. Bingham, A. Hyvärinen
- Computer Science
- Int. J. Neural Syst.
- 1 February 2000
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 valued… Expand
A Fast Fixed-Point Algorithm for Independent Component Analysis
- A. Hyvärinen, E. Oja
- Computer Science, Mathematics
- Neural Computation
- 1 October 1997
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 be… Expand