Rapid uncertainty computation with gaussian processes and histogram intersection kernels

Authors:
Alexander Freytag;Erik Rodner;Paul Bodesheim;Joachim Denzler
Affiliations:
Computer Vision Group, Friedrich Schiller University Jena, Germany;Computer Vision Group, Friedrich Schiller University Jena, Germany,Vision Group, ICSI, UC Berkeley, United States;Computer Vision Group, Friedrich Schiller University Jena, Germany;Computer Vision Group, Friedrich Schiller University Jena, Germany
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Year:
2012

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Abstract

An important advantage of Gaussian processes is the ability to directly estimate classification uncertainties in a Bayesian manner. In this paper, we develop techniques that allow for estimating these uncertainties with a runtime linear or even constant with respect to the number of training examples. Our approach makes use of all training data without any sparse approximation technique while needing only a linear amount of memory. To incorporate new information over time, we further derive online learning methods leading to significant speed-ups and allowing for hyperparameter optimization on-the-fly. We conduct several experiments on public image datasets for the tasks of one-class classification and active learning, where computing the uncertainty is an essential task. The experimental results highlight that we are able to compute classification uncertainties within microseconds even for large-scale datasets with tens of thousands of training examples.