Kernel Bandwidth Estimation for Nonparametric Modeling

A.G. Bors, N. Nasios

Research output: Contribution to journalArticlepeer-review


Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimation method for finding the bandwidth from a given data set. The proposed bandwidth estimation method is applied in three different computational-intelligence methods that rely on kernel density estimation: 1) scale space; 2) mean shift; and 3) quantum clustering. The third method is a novel approach that relies on the principles of quantum mechanics. This method is based on the analogy between data samples and quantum particles and uses the Schrodinger potential as a cost function. The proposed methodology is used for blind-source separation of modulated signals and for terrain segmentation based on topography information.
Original languageEnglish
Pages (from-to)1543 -1555
Number of pages13
JournalSystems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Issue number6
Early online date19 Jun 2009
Publication statusPublished - 1 Dec 2009


  • Bayesian estimation method
  • Schrodinger potential
  • blind-source separation
  • computational-intelligence methods
  • cost function
  • kernel bandwidth estimation
  • kernel density estimation
  • mean shift
  • modulated signals
  • nonparametric modeling
  • probability density modeling
  • quantum clustering
  • scale space
  • terrain segmentation
  • topography information
  • belief networks
  • blind source separation
  • image segmentation

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