Multiple kernel maximum margin criterion

Authors:
Quanquan Gu;Jie Zhou
Affiliations:
State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
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Abstract

Maximum Margin Criterion (MMC) is an efficient and robust feature extraction method, which has been proposed recently. Like other kernel methods, when MMC is extended to Reproducing Kernel Hilbert Space via kernel trick, its performance heavily depends on the choice of kernel. In this paper, we address the problem of learning the optimal kernel over a convex set of prescribed kernels for Kernel MMC (KMMC). We will give an equivalent graph based formulation of MMC, based on which we present Multiple Kernel Maximum Margin Criterion (MKMMC). Then we will show that MKMMC can be solved via alternative optimization schema. Experiments on benchmark image recognition data sets show that the proposed method outperforms KMMC via cross validation, as well as some state of the art methods.