Local Selection of Model Parameters in Probability Density Function Estimation

  • Ezequiel López-Rubio
  • Juan Miguel Ortiz-de-Lazcano-Lobato
  • Domingo López-Rodríguez
  • Enrique Mérida-Casermeiro
  • María del Carmen Vargas-González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Here we present a novel probability density estimation model. The classical Parzen window approach builds a spherical Gaussian density around every input sample. Our proposal selects a Gaussian specifically tuned for each sample, with an automated estimation of the local intrinsic dimensionality of the embedded manifold and the local noise variance. This leads to outperform other proposals where local parameter selection is not allowed, like the manifold Parzen windows.


Principal Direction Noise Variance Local Selection Finite Mixture Model Qualitative Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ezequiel López-Rubio
    • 1
  • Juan Miguel Ortiz-de-Lazcano-Lobato
    • 1
  • Domingo López-Rodríguez
    • 2
  • Enrique Mérida-Casermeiro
    • 2
  • María del Carmen Vargas-González
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of MálagaMálagaSpain
  2. 2.Department of Applied MathematicsUniversity of MálagaMálagaSpain

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