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Interior-Point Methods for Massive Support Vector Machines
Abstract
We investigate the use of interior-point methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a low-rank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category. An interesting feature of these particular problems is the volume of data, which can lead to quadratic programs with between 10 and 100 million variables and, if written explicitly, a dense Q matrix. Our code is based on OOQP, an object-oriented interior-point code, with the linear algebra specialized for the support vector machine application. For the targeted massive problems, all of the data is stored out of core and we overlap computation and input/output to reduce overhead. Results are reported for several linear support vector machine formulations demonstrating that the method is reliable and scalable.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Accuracy and Stability of Numerical Algorithms: Second Edition
Higham, N. J. (2002) · doi:10.1137/1.9780898718027 · Cited by 3,803
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Cutting-plane training of structural SVMs
Joachims, T., Finley, T., & Yu, C.-N. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5108-8 · Cited by 958
Support vector machines: hype or hallelujah?
Bennett, K. P., & Campbell, C. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/380995.380999 · Cited by 687
High dimensional data classification and feature selection using support vector machines
Ghaddar, B., & Naoum-Sawaya, J. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.08.040 · Cited by 334
Islam, N., Faheem, Y., Din, I. U., Talha, M., Guizani, M., & Khalil, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.059 · Cited by 154
Metadata
- Authors
- Ferris, M. C., & Munson, T. S.
- Venue
- SIAM Journal on Optimization
- Published
- 2002-01
- Volume
- 13
- Issue
- 3
- Pages
- 783-804
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W2065887373
- Cited by
- 186
- References
- 3
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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