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- Aapo Hyvärinen, Erkki Oja
- Neural Networks
- 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 and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the… (More)

- Aapo Hyvärinen, Erkki Oja
- Neural Computation
- 1997

We introduce a novel fast algorithm for Independent Component Analysis, which can be used for blind source separation and feature extraction. It is shown how a neural network learning rule can be transformed into a xed-point iteration, which provides an algorithm that is very simple, does not depend on any user-deened parameters, and is fast to converge to… (More)

- Lei Xu, Erkki Oja, Pekka Kultanen
- Pattern Recognition Letters
- 1990

- Lei Xu, Adam Krzyzak, Erkki Oja
- IEEE Trans. Neural Networks
- 1993

It is shown that frequency sensitive competitive learning (FSCL), one version of the recently improved competitive learning (CL) algorithms, significantly deteriorates in performance when the number of units is inappropriately selected. An algorithm called rival penalized competitive learning (RPCL) is proposed. In this algorithm, not only is the winner… (More)

- Erkki Oja, Stefan Harmeling, Luís B. Almeida
- Signal Processing
- 2004

Independent component analysis (ICA) aims at extracting unknown hidden factors/components from multivariate data using only the assumption that the unknown factors are mutually independent. Since the introduction of ICA concepts in the early 1980s in the context of neural networks and array signal processing, many new successful algorithms have been… (More)

- Erkki Oja
- Neural Networks
- 1992

- Erkki Oja
- Int. J. Neural Syst.
- 1989

- Ricardo Vigário, Jaakko Särelä, V. Jousmiki, Matti Hämäläinen, Erkki Oja
- IEEE Trans. Biomed. Engineering
- 2000

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for… (More)

- Aapo Hyv, Erkki Oja
- 1999

Imagine that you are in a room where two people are speaking simultaneously You have two microphones which you hold in di erent locations The microphones give you two recorded time signals which we could denote by x t and x t with x and x the amplitudes and t the time index Each of these recorded signals is a weighted sum of the speech signals emitted by… (More)

- Aapo Hyvärinen, Erkki Oja
- Signal Processing
- 1998

A number of neural learning rules have been recently proposed for Independent Component Analysis (ICA). The rules are usually derived from information-theoretic criteria such as maximum entropy or minimum mutual information. In this paper, we show that in fact, ICA can be performed by very simple Hebbian or anti-Hebbian learning rules, which may have only… (More)