An Approximate Linear Solver in Least Square Support Vector Machine Using Randomized Singular Value Decomposition  

作　　者：LIU Bing XIANG Hua 

机构地区：[1]School of Mathematics and Statistics,Wuhan University

出　　处：《Wuhan University Journal of Natural Sciences》2015年第4期283-290,共8页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China(10901125,11471253)

摘　　要：In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition（randomized SVD）. Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process.In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition（randomized SVD）. Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process.

关 键 词：least square support vector machine Nystr?m method Lanczos process randomized singular value decomposition 

分 类 号：O29[理学—应用数学]