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
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We present a nonparametric probability density estimation model. The classical Parzen window approach builds a spherical Gaussian density around every input sample. 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. Our proposal estimates the local principal directions to yield a specific Gaussian mixture component for each soft cluster. This leads to outperform other proposals where local parameter selection is not allowed and/or there are no smoothing strategies, like the manifold Parzen windows.