Multiple random subset-kernel learning

Authors:
Kenji Nishida;Jun Fujiki;Takio Kurita
Affiliations:
Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba Ibaraki, Japan;Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba Ibaraki, Japan;Faculty of Engineering, Hiroshima University, Higashi-Hiroshima Hiroshima, Japan
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

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Abstract

In this paper, the multiple random subset-kernel learning (MRSKL) algorithm is proposed. In MRSKL, a subset of training samples is randomly selected for each kernel with randomly set parameters, and the kernels with optimal weights are combined for classification. A linear support vector machine (SVM) is adopted to determine the optimal kernel weights; therefore, MRSKL is based on a hierarchical SVM. MRSKL outperforms a single SVM even when using a small number of samples (200 to 400 out of 20,000 training samples), while the SVM requires more than 4,000 support vectors.