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ICPR
2010
IEEE

Multiple Kernel Learning with High Order Kernels

8 years 9 months ago
Multiple Kernel Learning with High Order Kernels
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use "high order kernels" to enhance the learning of MKL when a set of original kernels are given. High order kernels are generated by the products of real power of the original kernels. We incorporate the original kernels and high order kernels into a unified localized kernel logistic regression model. To avoid over-fitting, we apply group LASSO regularization to the kernel coefficients of each training sample. Experiments on image classification prove that our approach outperforms many of the existing MKL approaches. Keywords-Multiple Kernel Learning; High Order Kernels; Image Classification.
Shuhui Wang, Shuqiang Jiang, Qingming Huang, Qi Ti
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where ICPR
Authors Shuhui Wang, Shuqiang Jiang, Qingming Huang, Qi Tian
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