Robust Nonparametric Probability Density Estimation by Soft Clustering

Authors:
Ezequiel López-Rubio;Juan Miguel Ortiz-De-Lazcano-Lobato;Domingo López-Rodríguez;María Carmen Vargas-Gonzalez
Affiliations:
School of Computing, University of Málaga, Málaga, Spain 29071;School of Computing, University of Málaga, Málaga, Spain 29071;School of Computing, University of Málaga, Málaga, Spain 29071;School of Computing, University of Málaga, Málaga, Spain 29071
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

A method to estimate the probability density function of multivariate distributions is presented. The classical Parzen window approach builds a spherical Gaussian density around every input sample. This choice of the kernel density yields poor robustness for real input datasets. We use multivariate Student-t distributions in order to improve the adaptation capability of the model. Our method has a first stage where hard neighbourhoods are determined for every sample. Then soft clusters are considered to merge the information coming from several hard neighbourhoods. Hence, a specific mixture component is learned for each soft cluster. This leads to outperform other proposals where the local kernel is not as robust and/or there are no smoothing strategies, like the manifold Parzen windows.