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| Classification Algorithm of l2-norm LS-SVM via Coordinate Descent |
| LIU Jian-Wei1,FU Jie1,WANG Shao-Lei2,LUO Xiong-Lin1 |
1.Department of Automation,China University of Petroleum,Beijing 102249
2.Wenmioil Production Plant of Tuha Oilfield Branch,China National Petroleum Corporation,Shanshan 838202 |
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Abstract The coordinate descent approach for l2 norm regulated least square support vector machine is studied. The datasets involved in the objective function for machine learning have larger data scale than the memory size has in image processing,human genome analysis,information retrieval,data management,and data mining. Recently,the coordinate descent method for large-scale linear SVM has good classification performance on large scale datasets. In this paper, the results of the work are extended to the least square support vector machine,and the coordinate descent approach for l2 norm regulated least square support vector machine is proposed. The vector optimization of the LS-SVM objective function is reduced to single variable optimization by the proposed algorithm. The experimental results on high-dimension small-sample datasets,middle-scale datasets and large-scale datasets demonstrate its effectiveness. Compared to the state-of-the-art LS-SVM classifiers,the proposed method can be a good candidate when data cannot fit in memory.
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Received: 13 February 2012
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2013, Vol. 26 
