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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… (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. We show how a neural network learning rule can be… (More)
- Erkki Oja
- Journal of mathematical biology
- 1982
A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to… (More)
- Lei Xu, Erkki Oja, Pekka Kultanen
- 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… (More)
- Erkki Oja
- 1983
- Erkki Oja
- Neural Networks
- 1992
-Many neural network realizations have been recently proposed for the statistical technique of Principal Component Analysis ( PCA ). Explicit connections between numerical constrained adaptive… (More)
- Erkki Oja
- Int. J. Neural Syst.
- 1989
- Zhijian Yuan, Erkki Oja
- SCIA
- 2005
In image compression and feature extraction, linear expansions are standardly used. It was recently pointed out by Lee and Seung that the positivity or non-negativity of a linear expansion is a very… (More)
- Zhirong Yang, Erkki Oja
- IEEE Transactions on Neural Networks
- 2010
A variant of nonnegative matrix factorization (NMF) which was proposed earlier is analyzed here. It is called projective nonnegative matrix factorization (PNMF). The new method approximately… (More)