- Veto-Consensus Multiple Kernel Learning
- 30th AAAI Conference on Artificial Intelligence
- Book/source title
- Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16): Hyatt Regency Phoenix, February 12, 2016-February 17, 2016
- Pages (from-to)
- Palo Alto, California: Association for the Advancement of Artificial Intelligence
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The proposed configuration is a natural fit for domain description and learning with hidden subgroups. We first provide generalization risk bound in terms of the Rademacher complexity of the classifier, and then a large margin multi-ν learning objective with tunable training error bound is formulated. Seeing that the corresponding optimization is non-convex and existing methods severely suffer from local minima, we establish a new algorithm, namely Parametric Dual Descent Procedure (PDDP) that can approach global optimum with guarantees. The bases of PDDP are two theorems that reveal the global convexity and local explicitness of the parameterized dual optimum, for which a series of new techniques for parametric program have been developed. The proposed method is evaluated on extensive set of experiments, and the results show significant improvement over the state-of-the-art approaches.
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