Aapo Hyvärinen: Publications

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Brain imaging data analysis Bayesian networks & SEM Estimation theory Natural image statistics The FastICA algorithm Independent component analysis: misc. Unsupervised learning: misc. Experimental vision research   Publications front page Back to home page

Unsupervised learning: misc.

[Papers on various topics in unsupervised learning.]

M. U. Gutmann and A. Hyvärinen. Extracting coactivated features from multiple datasets . Proc. Int. Conf. on Artificial Neural Networks (ICANN2011), Helsinki, Finland, 2011.
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[A method of data fusion, in which we find common features in two or more datasets.]

M. Gutmann and A. Hyvärinen. Learning reconstruction and prediction of natural stimuli by a population of spiking neurons Proc. European Symposium on Artificial Neural Networks (ESANN2009), Bruges, Belgium, 2009.
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[Shows how to find a statistically motivated representation in a population of neurons using a biophysically motivated neuron model.]

M. Gutmann, A. Hyvärinen, and K. Aihara. Learning encoding and decoding filters for data representation with a spiking neuron Proc. Int. Joint Conf. on Neural Networks (IJCNN2008), Hong Kong, 2008.
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[A predecessor of the paper above: A method similar to PCA but using a biological model of a neuron.]

A. Hyvärinen. Unsupervised learning of an embodied representation for action selection. Proc. European Conference on Cognitive Science (EuroCogSci07), Delphi, Greece, pp. 658-665, 2007.
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[A method which an agent can use to learn a representation of the world to support action.]

A. Hyvärinen. Behavioural priors: Learning to search efficiently in action planning. Proc. European Conference on Cognitive Science (EuroCogSci07), Delphi, Greece, pp. 324--328, 2007.
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[Proposes how planning can be improved by building a prior model of useful behavioural sequences.]

M.A. Vicente, P.O. Hoyer and A. Hyvärinen. Equivalence of some common linear feature extraction techniques for appearence-based object recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5):896-900, 2007.
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[Shows that ICA features are often equivalent, in terms of classification performance, to the features given by whitening, and discusses how ICA features should be used to improve classification.]

A. Hyvärinen and J. Perkiö. Learning to segment any random vector. Proc. Int. Joint Conf. on Neural Networks (IJCNN2006), pp. 4167-4172, Vancouver, Canada, 2006.
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[Proposes a probabilistic framework for learning to segment observations of any random vector. A generalization of models of image segmentation.]

A. Hyvärinen. Consistency of pseudolikelihood estimation of fully visible Boltzmann machines. Neural Computation, 18(10):2283-2292, 2006.
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[A very simple estimation method for fully visible BM's, with guaranteed consistency.]