Expanding Gaussian kernels for multivariate conditional density estimation

We demonstrate fundamental problems with the standard use of Gaussian kernels (SGKs) for estimating f(m|x) from sparse training data (x/sup i/,m/sup i/). We develop a new method that overcomes these considerations using Gaussian kernels with expanding covariances (EGKs) combined through Bayesian ana...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1998-01, Vol.46 (1), p.269-275
Main Authors: Davis, D.T., Jenq-Neng Hwang
Format: Article
Language:English
Subjects:
Applied sciences
Atmospheric measurements
Atmospheric modeling
Bayesian methods
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Geophysical measurements
Information, signal and communications theory
Kernel
Moisture measurement
Satellites
Signal and communications theory
Signal, noise
Soil measurements
Standards organizations
Telecommunications and information theory
Training data
Citations: Items that this one cites
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Summary:We demonstrate fundamental problems with the standard use of Gaussian kernels (SGKs) for estimating f(m|x) from sparse training data (x/sup i/,m/sup i/). We develop a new method that overcomes these considerations using Gaussian kernels with expanding covariances (EGKs) combined through Bayesian analysis. In addition, we demonstrate that for a synthetic problem, EGKs perform better qualitatively and quantitatively with respect to the Kullback-Leibler criteria.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.651234