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Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels
Erik Rodner, Alexander Freytag, Paul Bodesheim, and Joachim Denzler
Computer Vision Group, Friedrich Schiller University Jena, Germanyfirstname.lastname@uni-jena.de
http://www.inf-cv.uni-jena.de
Abstract. We present how to perform exact large-scale multi-class Gaussian process classification with parameterized histogram intersection kernels. In contrast to previous approaches, we use a full Bayesian model without any sparse approximation techniques, which allows for learning in sub-quadratic and classification in constant time. To handle the additional model flexibility induced by parameterized kernels, our approach is able to optimize the parameters with large-scale training data. A key ingredient of this optimization is a new efficient upper bound of the negative Gaussian process log-likelihood. Experiments with image categorization tasks exhibit high performance gains with flexible kernels as well as learning within a few minutes and classification in microseconds for databases, where exact Gaussian process inference was not possible before.
Keywords: Large-scale Gaussian Processes, Histogram Intersection Kernels, Hyperparameter Optimization, Bayesian Modeling
LNCS 7575, p. 85 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012