A Randomized Heuristic for Kernel Parameter Selection with Large-scale Multi-class Data

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2011

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Over the past few years kernel methods have gained a tremendous amount of attention as existing linear algorithms can easily be extended to account for highly non-linear data in a computationally efficient manner. Unfortunately most kernels require careful tuning of intrinsic parameters to correctly model the distribution of the underlying data. For large-scale problems the multiplicative scaling in time complexity imposed by introducing free parameters in a crossvalidation setup will prove computationally infeasible, often leaving pure ad-hoc estimates as the only option. In this contribution we investigate a novel randomized approach for kernel parameter selection in large-scale multi-class data. We fit a minimum enclosing ball to the class means in Reproducing Kernel Hilbert Spaces (RKHS), and use the radius as a quality measure of the space, defined by the kernel parameter. We apply the developed algorithm to a computer vision paradigm where the objective is to recognize 72:000 objects among 1:000 classes. Compared to other distance metrics in the RKHS we find that our randomized approach provides better results together with a highly competitive time complexity.
Original languageEnglish
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Publication date2011
ISBN (print)978-1-4577-1621-8
ISBN (electronic)978-1-4577-1622-5
StatePublished - 2011
Event2011 IEEE International Workshop on Machine Learning for Signal Processing - Beijing, China


Conference2011 IEEE International Workshop on Machine Learning for Signal Processing
Period01/01/2011 → …
Internet address
CitationsWeb of Science® Times Cited: No match on DOI
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ID: 5886734