Local selection of model parameters in probability density function estimation

Authors:
Ezequiel López-Rubio;Juan Miguel Ortiz-de-Lazcano-Lobato;Domingo López-Rodríguez;Enrique Mérida-Casermeiro;María del Carmen Vargas-González
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Málaga, Málaga, Spain;Department of Computer Science and Artificial Intelligence, University of Málaga, Málaga, Spain;Department of Applied Mathematics, University of Málaga, Málaga, Spain;Department of Applied Mathematics, University of Málaga, Málaga, Spain;Department of Computer Science and Artificial Intelligence, University of Málaga, Málaga, Spain
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

Citing 2
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Smooth estimators of distribution and density functions

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Neural Networks for Pattern Recognition

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Abstract

Here we present a novel probability density estimation model. The classical Parzen window approach builds a spherical Gaussian density around every input sample. Our proposal selects a Gaussian specifically tuned for each sample, with an automated estimation of the local intrinsic dimensionality of the embedded manifold and the local noise variance. This leads to outperform other proposals where local parameter selection is not allowed, like the manifold Parzen windows.