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2013 | OriginalPaper | Chapter

77. Efficient Mixed-Norm Multiple Kernel Learning

Authors : Yan Wei, Han-shu Qin, Shao-hua Zeng

Published in: Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012

Publisher: Springer London

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Abstract

Multiple kernels learning (MKL) is a hot topic in the current kernel machine learning field which aims at find a convexity linear combination of based kernels. Current MKL methods encourage spare kernel coefficients combination, unfortunately, when features encode orthogonal data, spareness tends to select only a few kernels, and may discards useful information which lead to poor generalization performance. In this paper, we presented an efficient multiple kernels learning method based on mix-norm in which sparseness and nonsparseness can be compromised using a mixing regularization. Both SVM and MKL could be regarded as special cases of EMNMKL. Then, we developed a rapid gradient descent algorithm to deal with the problem. Simulation experiment results show that the EMNMKL rapidly converges and the average testing accuracy demonstrates that EMNMKL algorithm clearly outperforms SVM and MKL.

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Metadata
Title
Efficient Mixed-Norm Multiple Kernel Learning
Authors
Yan Wei
Han-shu Qin
Shao-hua Zeng
Copyright Year
2013
Publisher
Springer London
Book
Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012

Print ISBN: 978-1-4471-4855-5

Electronic ISBN: 978-1-4471-4856-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4471-4856-2_77