A fast method of feature extraction for kernel MSE

Authors:
Yong-Ping Zhao;Zhong-Hua Du;Zhi-An Zhang;Hai-Bo Zhang
Affiliations:
ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, PR China;ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, PR China;ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, PR China and College of Automation and Engineering, Nanjing University of Aeronautics and Astronau ...;College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China
Venue:
Neurocomputing
Year:
2011

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Abstract

In this paper, a fast method of selecting features for kernel minimum squared error (KMSE) is proposed to mitigate the computational burden in the case where the size of the training patterns is large. Compared with other existent algorithms of selecting features for KMSE, this iterative KMSE, viz. IKMSE, shows better property of enhancing the computational efficiency without sacrificing the generalization performance. Experimental reports on the benchmark data sets, nonlinear autoregressive model and real problem address the efficacy and feasibility of the proposed IKMSE. In addition, IKMSE can be easily extended to classification fields.