JSW 2012 Vol.7(9): 2076-2082 ISSN: 1796-217X
doi: 10.4304/jsw.7.9.2076-2082
doi: 10.4304/jsw.7.9.2076-2082
An Online Kernel Learning Algorithm based on Orthogonal Matching Pursuit
ShiLei Zhao1, Peng Wu2, and YuPeng Liu1
1School of Software, Harbin University of Science and Technology, Harbin, china
2College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, china
Abstract—Matching pursuit algorithms learn a function that is weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the least-squares sense. Experimental result shows that it is an effective method, but the drawbacks are that this algorithm is not appropriate to online learning or estimating the strongly nonlinear functions. In this paper, we present a kind of online kernel learning algorithm based on orthogonal matching pursuit. The orthogonal matching pursuit is employed not only to guide our online learning algorithm to estimate the target function but also to keep control of the sparsity of the solution. And the introduction of “kernel trick” can effective reduce the error when it is used to estimate the nonlinear functions. At last, a kind of nonlinear two-dimensional “sinc” function is used to test our algorithm and the results are compared with the well-known SVMTorch on Support Vectors percent and root mean square error which approve that our online learning algorithm is effective.
Index Terms—orthogonal matching pursuit; kernel trick; online learning.
2College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, china
Abstract—Matching pursuit algorithms learn a function that is weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the least-squares sense. Experimental result shows that it is an effective method, but the drawbacks are that this algorithm is not appropriate to online learning or estimating the strongly nonlinear functions. In this paper, we present a kind of online kernel learning algorithm based on orthogonal matching pursuit. The orthogonal matching pursuit is employed not only to guide our online learning algorithm to estimate the target function but also to keep control of the sparsity of the solution. And the introduction of “kernel trick” can effective reduce the error when it is used to estimate the nonlinear functions. At last, a kind of nonlinear two-dimensional “sinc” function is used to test our algorithm and the results are compared with the well-known SVMTorch on Support Vectors percent and root mean square error which approve that our online learning algorithm is effective.
Index Terms—orthogonal matching pursuit; kernel trick; online learning.
Cite: ShiLei Zhao, Peng Wu, and YuPeng Liu, "An Online Kernel Learning Algorithm based on Orthogonal Matching Pursuit," Journal of Software vol. 7, no. 9, pp. 2076-2082, 2012.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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