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An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines

  • Fabian Gieseke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9432)

Abstract

The concept of semi-supervised support vector machines extends classical support vector machines to learning scenarios, where both labeled and unlabeled patterns are given. In recent years, such semi-supervised extensions have gained considerable attention due to their huge potential for real-world applications with only small amounts of labeled data. While being appealing from a practical point of view, semi-supervised support vector machines lead to a combinatorial optimization problem that is difficult to address. Many optimization approaches have been proposed that aim at tackling this task. However, the computational requirements can still be very high, especially in case large data sets are considered and many model parameters need to be tuned. A recent trend in the field of big data analytics is to make use of graphics processing units to speed up computationally intensive tasks. In this work, such a massively-parallel implementation is developed for semi-supervised support vector machines. The experimental evaluation, conducted on commodity hardware, shows that valuable speed-ups of up to two orders of magnitude can be achieved over a standard single-core CPU execution.

Keywords

Semi-supervised support vector machines Non-convex optimization Graphics processing units Big data analytics 

Notes

Acknowledgements

The author would like to thank the anonymous reviewers for their careful reading and detailed comments. This work has been supported by the Radboud Excellence Initiative of the Radboud University Nijmegen. The author also would like to thank NVIDIA for generous hardware donations.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands

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