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.
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López-Rubio, E., Ortiz-de-Lazcano-Lobato, J.M., López-Rodríguez, D., del Carmen Vargas-Gonzalez, M. (2008). Robust Nonparametric Probability Density Estimation by Soft Clustering. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_17
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DOI: https://doi.org/10.1007/978-3-540-87536-9_17
Publisher Name: Springer, Berlin, Heidelberg
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