Multivariate online kernel density estimation with Gaussian kernels

We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach...

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Veröffentlicht in:	Pattern recognition Jg. 44; H. 10; S. 2630 - 2642
Hauptverfasser:	Kristan, Matej, Leonardis, Aleš, Skočaj, Danijel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.10.2011 Elsevier
Schlagworte:	Applied sciences Artificial intelligence Complexity Compressing Computer science; control theory; systems Density Exact sciences and technology Gaussian Gaussian mixture models Information, signal and communications theory Kernel density estimation Kernels On-line systems Online Online models Pattern recognition Pattern recognition. Digital image processing. Computational geometry Probability density estimation Signal processing State of the art Telecommunications and information theory Gaussian mixture models Probability density estimation Kernel density estimation Online models Performance evaluation Density estimation State of the art Non parametric method Mixture theory Updating Pattern recognition Kernel method Statistical method Probability density Probabilistic assessment Gaussian process On line processing Probability density function
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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