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ICANN
2009
Springer

Bayesian Estimation of Kernel Bandwidth for Nonparametric Modelling

9 years 11 months ago
Bayesian Estimation of Kernel Bandwidth for Nonparametric Modelling
Kernel density estimation (KDE) has been used in many computational intelligence and computer vision applications. In this paper we propose a Bayesian estimation method for finding the bandwidth in KDE applications. A Gamma density function is fitted to distributions of variances of K-nearest neighbours data populations while uniform distribution priors are assumed for K. A maximum log-likelihood approach is used to estimate the parameters of the Gamma distribution when fitted to the local data variance. The proposed methodology is applied in three different KDE approaches: kernel sum, mean shift and quantum clustering. The third method relies on the Schr¨odinger partial differential equation and uses the analogy between the potential function that manifests around particles, as defined in quantum physics, and the probability density function corresponding to data. The proposed algorithm is applied to artificial data and to segment terrain images.
Adrian G. Bors, Nikolaos Nasios
Added 25 Jul 2010
Updated 25 Jul 2010
Type Conference
Year 2009
Where ICANN
Authors Adrian G. Bors, Nikolaos Nasios
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