Trading Convexity for Scalability

Collobert, R; Sinz, F; Weston, J; Bottou, L

doi:10.1145/1143844.1143870

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Trading Convexity for Scalability

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Sinz, F
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Trading Convexity for Scalability. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 201-208). New York, NY, USA: ACM Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D141-E

Abstract

Convex learning algorithms, such as Support Vector Machines (SVMs), are
often seen as highly desirable because they offer strong practical
properties and are amenable to theoretical analysis. However, in this work
we show how non-convexity can provide scalability advantages over
convexity. We show how concave-convex programming can be applied to produce
(i) faster SVMs where training errors are no longer support vectors, and
(ii) much faster Transductive SVMs.