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Mixture Density Mercer Kernels: A Method to Learn Kernels

Metadata Updated: February 6, 2026

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.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

Downloads & Resources

Dates

Metadata Created Date November 12, 2020
Metadata Updated Date February 6, 2026
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date February 6, 2026
Publisher Dashlink
Maintainer
Identifier DASHLINK_157
Data First Published 2010-09-22
Data Last Modified 2025-03-31
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id acc3f992-8169-465d-9439-cf74bac238e7
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://c3.nasa.gov/dashlink/resources/157/
Program Code 026:029
Source Datajson Identifier True
Source Hash 0c9427ee6c756b92a408e04ab334700386d319c4284b40113e8414b44ee70c88
Source Schema Version 1.1

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