Smooth estimators of distribution and density functions
Computational Statistics & Data Analysis - Second special issue on optimization techniques in statistics
Weighted Parzen Windows for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Parzen Density Estimation Using Clustering-Based Branch and Bound
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detector of image orientation based on Borda Count
Pattern Recognition Letters
Information cut for clustering using a gradient descent approach
Pattern Recognition
Geometric and photometric invariant distinctive regions detection
Information Sciences: an International Journal
Weighted Sub-Gabor for face recognition
Pattern Recognition Letters
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
Blind spectral unmixing by local maximization of non-Gaussianity
Signal Processing
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Location Estimation for Probability Density Function Learning
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Multivariate online kernel density estimation with Gaussian kernels
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 neighborhoods are determined for every sample. Then soft clusters are considered to merge the information coming from several hard neighborhoods. 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.