Solving Linear SVMs with Multiple 1D Projections

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Schneider, J., Bogojeska, J., & Vlachos, M. (2014). Solving Linear SVMs with Multiple 1D Projections. Paper presented at the Conference on Information and Knowledge Management (CIKM).


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We present a new methodology for solving linear Support Vector Machines (SVMs) that capitalizes on multiple 1D projections. We show that the approach approximates the optimal solution with high accuracy and comes with analytical guarantees. Our solution adapts on methodologies from random projections, exponential search, and coordi- nate descent. In our experimental evaluation, we compare our approach with the popular liblinear SVM library. We demonstrate a signi?cant speedup on various benchmarks. At the same time, the new methodology provides a comparable or better approximation factor of the optimal solution and exhibits smooth convergence properties. Our results are accompanied by bounds on the time complexity and accuracy.



  • Institut für Wirtschaftsinformatik
  • Hilti Lehrstuhl für Business Process Management

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