Support vector machine classification with indefinite kernels

  • We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as noisy observations of a true Mercer kernel. Our formulation keeps the problem convex and relatively large problems can be solved efficiently using the projected gradient or analytic center cutting plane methods. We compare the performance of our technique with other methods on several standard data sets.

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Metadaten
Author:Ronny Luss, Alexandre d’Aspremont
DOI:https://doi.org/10.1007/s12532-009-0005-5
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2009
Tag:Software; Theoretical Computer Science
Volume:1
Issue:2-3
Page Number:22
First Page:97
Last Page:118
Mathematical Programming Computation :MPC 2009 - Issue 2-3
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