High-Performance Parallel Support Vector Machine Training

Kristian Woodsend () and Jacek Gondzio ()
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Kristian Woodsend: University of Edinburgh
Jacek Gondzio: University of Edinburgh

A chapter in Parallel Scientific Computing and Optimization, 2009, pp 83-92 from Springer

Abstract: Abstract Support vector machines are a powerful machine learning technology, but the training process involves a dense quadratic optimization problem and is computationally expensive. We show how the problem can be reformulated to become suitable for high-performance parallel computing. In our algorithm, data is pre-processed in parallel to generate an approximate low-rank Cholesky decomposition. Our optimization solver then exploits the problem’s structure to perform many linear algebra operations in parallel, with relatively low data transfer between processors, resulting in excellent parallel efficiency for very-large-scale problems.

Keywords: Support Vector Machine; Interior Point Method; Kernel Matrix; Cholesky Decomposition; Linear Support Vector Machine (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-09707-7_7

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DOI: 10.1007/978-0-387-09707-7_7

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