Mixture Density Mercer Kernels

We present a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate. We show how to convert the ensemble estimates into a Mercer Kernel, describe the properties of this new kernel function, and give examples of the performance of this kernel on unsupervised clustering of synthetic data and also in the domain of unsupervised multispectral image understanding.

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Additional Info

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Maintainer Ashok Srivastava
Last Updated March 31, 2025, 17:46 (UTC)
Created March 31, 2025, 17:46 (UTC)
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identifier DASHLINK_118
issued 2010-09-10
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modified 2020-01-29
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