Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Margin and Radius Based Multiple Kernel Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Learning bounds for support vector machines with learned kernels
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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Recent research has shown the benefit of incorporating the radius of the Minimal Enclosing Ball (MEB) of training data into Multiple Kernel Learning (MKL). However, straightforwardly incorporating this radius leads to complex learning structure and considerably increased computation. Moreover, the notorious sensitivity of this radius to outliers can adversely affect MKL. In this paper, instead of directly incorporating the radius of MEB, we incorporate its close relative, the trace of data scattering matrix, to avoid the above problems. By analyzing the characteristics of the resulting optimization, we show that the benefit of incorporating the radius of MEB can be fully retained. More importantly, our algorithm can be effortlessly realized within the existing MKL framework such as SimpleMKL. The mere difference is the way to normalize the basic kernels. Although this kernel normalization is not our invention, our theoretic derivation uncovers why this normalization can achieve better classification performance, which has not appeared in the literature before. As experimentally demonstrated, our method achieves the overall best learning performance in various settings. In another perspective, our work improves SimpleMKL to utilize the information of the radius of MEB in an efficient and practical way.