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International Journal of Computer Vision

27.07.2016

Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2017

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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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Fußnoten
1
Note that in the remainder of the article, the term uncertainty refers to classification uncertainty, and not to the query strategy introduced by Kapoor et al. (2010).
 
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Metadaten
Titel
Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks
Publikationsdatum
27.07.2016
Erschienen in
International Journal of Computer Vision / Ausgabe 2/2017
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-016-0929-y

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