Mixture Density Mercer Kernels: A Method to Learn Kernels
Shared by Ashok Srivastava, updated on Sep 22, 2010
Summary
- Author(s) :
- Ashok Srivastava
- Abstract
This paper presents 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.
- Publication Name
- Proceedings of the SIAM Data Mining Conference
- Publication Location
- N/A
- Year Published
- 2004
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