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Multivariate online kernel density estimation with Gaussian kernels
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DOI：10.1016/j.patcog.2011.03.019.png)
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Abstract
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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 and a compression/revitalization scheme which maintains the KDE's complexity low. We compare the proposed online KDE to the state-of-the-art approaches on examples of estimating stationary and non-stationary distributions, and on examples of classification. The results show that the online KDE outperforms or achieves a comparable performance to the state-of-the-art and produces models with a significantly lower complexity while allowing online adaptation. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Online models
Probability density estimation
Kernel density estimation
Gaussian mixture models
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