Supervised kernel self-organizing map

Authors:
Dongjun Yu;Jun Hu;Xiaoning Song;Yong Qi;Zhenmin Tang
Affiliations:
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China,Changshu Institute, Nanjing University of Science and Technology, Changshu, China;School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China;School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China,Changshu Institute, Nanjing University of Science and Technology, Changshu, China;School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

We generalize the traditional supervised self-organizing map to supervised kernel self-organizing map by incorporating the kernel function to further improve its capability of solving non-linear problems. The kernel function maps the low-dimensional input space to high-dimensional feature space thus potentially makes the complex non-linear structure in the input space much easier in the mapped feature space. Qualitative and quantitative analysis of the experimental results on the two benchmark datasets illustrate the effectiveness of the proposed method.