Conference paper accepted: Robust nonparametric probability density estimation by soft clustering

The work Robust nonparametric probability density estimation by soft clustering has been published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (5163 LNCS), PART 1, pp. 155-164.

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. © Springer-Verlag Berlin Heidelberg 2008.

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