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.
Citation
E. López-Rubio, J. Ortiz-De-Lazcano-Lobato, D. López-Rodríguez, et al. “Robust nonparametric probability density estimation by soft clustering”. In: Artificial Neural Networks - ICANN 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I. Ed. by V. K. á, R. Neruda and J. Koutník. Vol. 5163 LNCS. Lecture Notes in Computer Science PART 1. cited By 0; Conference of 18th International Conference on Artificial Neural Networks, ICANN 2008 ; Conference Date: 3 September 2008 Through 6 September 2008; Conference Code:73798. Prague: Springer, 2008, pp. 155-164. DOI: 10.1007/978-3-540-87536-9_17. URL: https://doi.org/10.1007/978-3-540-87536-9_17.
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- J. M. Pérez (2009). SINAI : Self-Organizing Neural Systems for Internet TIN 2006-07362.