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    • Mixture Density Mercer Kernels: A Method to Learn Kernels

    Metadata Updated: February 20, 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 20, 2026
    Data Update Frequency irregular

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date November 12, 2020
    Metadata Updated Date February 20, 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 d4d83590-f322-40f1-b828-968f8828f1a3
    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 0737f0103540b59bca90b26e209f62d6aadeb4c98d8e9c747e75a8894bf1311f
    Source Schema Version 1.1

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