NDLI: PASS-GP: Predictive active set selection for Gaussian processes

Content Provider	IEEE Xplore Digital Library
Author	Henao, R. Winther, O.
Copyright Year	2010
Description	Author affiliation: DTU Informatics, Technical University of Denmark, Denmark (Henao, R.; Winther, O.)
Abstract	We propose a new approximation method for Gaussian process (GP) learning for large data sets that combines inline active set selection with hyperparameter optimization. The predictive probability of the label is used for ranking the data points. We use the leave-one-out predictive probability available in GPs to make a common ranking for both active and inactive points, allowing points to be removed again from the active set. This is important for keeping the complexity down and at the same time focusing on points close to the decision boundary. We lend both theoretical and empirical support to the active set selection strategy and marginal likelihood optimization on the active set. We make extensive tests on the USPS and MNIST digit classification databases with and without incorporating invariances, demonstrating that we can get state-of-the-art results (e.g.0.86% error on MNIST) with reasonable time complexity.
Starting Page	148
Ending Page	153
File Size	132617
Page Count	6
File Format	PDF
ISBN	9781424478750
ISSN	15512541
e-ISBN	9781424478774
e-ISBN	9781424478767
DOI	10.1109/MLSP.2010.5589264
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-29
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Training Cavity resonators Support vector machines Prediction algorithms Optimization Gaussian processes
Content Type	Text
Resource Type	Article

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