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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:

2018年04期

Page:

65-

Research Field:

·数学与计算机科学·

Publishing date:

Info

Title:

Localized GEPSVM Based on Mahalanobis Metric

Author(s):

Zhou Jianhang¹; Yang Xubing¹; Zhang Fuquan¹; Ye Qiaolin¹; Xu Dengping²

(1.College of Information Science and Technology,Nanjing Forestry University,Nanjing 210037,China)(2.Survey & Planning Institute of State Forestry Administration,Beijing 100714,China)

Keywords:

proximal support vector machine; generalized eigenvalues; Mahalanobis metric; convex hull

PACS:

TP391

DOI:

10.3969/j.issn.1001-4616.2018.04.011

Abstract:

GEPSVM(Proximal Support Vector Machine via Generalized Eigenvalues)have been played more attention in machine learning and pattern recognition. It adopts data fitting to construct classifier,and further leading to two Generalized Eigenvalue problems. One of its variants is Localized GEPSVM,shortly LGEPSVM. Instead of the closest nonparallel planes of GEPSVM,LGEPSVM classifies an unknown sample to the closest convex hulls on the projection plane. Experimental results show that LGEPSVM able to achieve comparable or even better test correctness than GEPSVM. However,due to training convex hull,LGEPSVM would cost much time in training stage. To speed training LGEPSVM,in this paper,we propose a new version LGEPSVM,termed as MLGEPSVM,based on Mahalanobis metric. Concretely,MLGEPSVM aims to find two ellipsoidal convex hulls,and then classify the samples to the class corresponding to its closest ellipsoid. Compared to LGEPSVM and GEPSVM,advantages of MLGEPSVM lie in three aspects:(1)calculation method of ellipsoid convex hull,(2)faster classification speed,and(3)less storage requirement,only ellipsoid convex hull of each class will be stored(the sample center and covariance matrix). Finally,analysis and experiments on artificial and UCI benchmark datasets will validate our foresaid superiorities.

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Last Update: 2018-12-30