Description
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.
Resources
| Name | Format | Description | Link |
|---|---|---|---|
| Download this dataset | HTML | Query and order satellite images, aerial photographs, and cartographic products through the U.S. Geological Survey. Log in as a guest or as a registered user. Registered users have access to more features than guests do. If you plan on using EarthExplorer frequently, you may wish to register. Please note that this site uses Session Cookies and Java applets. | Open resource |
| Probabilistic_Kernels_2003.pdf | Paper | Open resource |