Smooth estimators of distribution and density functions
Computational Statistics & Data Analysis - Second special issue on optimization techniques in statistics
Mixtures of probabilistic principal component analyzers
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
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We present a method to estimate the probability density function of multivariate distributions. Standard Parzen window approaches use the sample mean and the sample covariance matrix around every input vector. This choice yields poor robustness for real input datasets. We propose to use the L1-median to estimate the local mean and covariance matrix with a low sensitivity to outliers. In addition to this, a smoothing phase is considered, which improves the estimation by integrating the information from several local clusters. Hence, a specific mixture component is learned for each local 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.