Efficient Gaussian process classification using random decision forests

Authors:
B. Fröhlich;E. Rodner;M. Kemmler;J. Denzler
Affiliations:
Chair for Computer Vision, Friedrich-Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Friedrich-Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Friedrich-Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Friedrich-Schiller University of Jena, Jena, Germany
Venue:
Pattern Recognition and Image Analysis
Year:
2011

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Abstract

Gaussian Processes are powerful tools in machine learning which offer wide applicability in regression and classification problems due to their non-parametric and non-linear behavior. However, one of their main drawbacks is the training time complexity which scales cubically with the number of samples. Our work addresses this issue by combining Gaussian Processes with Randomized Decision Forests to enable fast learning. An important advantage of our method is its simplicity and the ability to directly control the trade-off between classification performance and computation speed. Experiments on an indoor place recognition task show that our method can handle large training sets in reasonable time while retaining a good classification accuracy.