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  Large-scale Gaussian process inference with generalized histogram intersection kernels for visual recognition tasks

Rodner, E., Freytag, A., Bodesheim, P., Fröhlich, B., & Denzler, J. (2017). Large-scale Gaussian process inference with generalized histogram intersection kernels for visual recognition tasks. International Journal of Computer Vision, 121(2), 253-280. doi:10.1007/s11263-016-0929-y.

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BGC2490.pdf (Publisher version), 2MB
 
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Rodner, Erik, Author
Freytag, Alexander, Author
Bodesheim, Paul1, Author           
Fröhlich, Björn, Author
Denzler, Joachim, Author
Affiliations:
1Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938312              

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 Abstract: We present new methods for fast Gaussian process (GP) inference in large-scale scenarios including exact multi-class classification with label regression, hyperparameter optimization, and uncertainty prediction. In contrast to previous approaches, we use a full Gaussian process model without sparse approximation techniques. Our methods are based on exploiting generalized histogram intersection kernels and their fast kernel multiplications. We empirically validate the suitability of our techniques in a wide range of scenarios with tens of thousands of examples. Whereas plain GP models are intractable due to both memory consumption and computation time in these settings, our results show that exact inference can indeed be done efficiently. In consequence, we enable every important piece of the Gaussian process framework—learning, inference, hyperparameter optimization, variance estimation, and online learning—to be used in realistic scenarios with more than a handful of data.

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 Dates: 20162016-07-272017
 Publication Status: Issued
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 Identifiers: Other: BGC2490
DOI: 10.1007/s11263-016-0929-y
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Title: International Journal of Computer Vision
  Other : Int. J. Comput. Vis.
Source Genre: Journal
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Publ. Info: Hingham, Mass. : Kluwer Academic Publishers
Pages: - Volume / Issue: 121 (2) Sequence Number: - Start / End Page: 253 - 280 Identifier: ISSN: 0920-5691
CoNE: https://pure.mpg.de/cone/journals/resource/954925564668