Improved Generalized Eigenvalue Proximal Support Vector Machine

Author

Yuan-Hai Shao ; Nai-Yang Deng ; Wei-Jie Chen ; Zhen Wang

Author_Institution

Zhijiang Coll., Zhejiang Univ. of Technol., Hangzhou, China

Volume

20

Issue

3

fYear

2013

fDate

Mar-13

Firstpage

213

Lastpage

216

Abstract

In this letter, we propose an improved version of generalized eigenvalue proximal support vector machine (GEPSVM), called IGEPSVM for short. The main improvements are 1) the generalized eigenvalue decomposition is replaced by the standard eigenvalue decomposition, resulting in simpler optimization problems without the possible singularity. 2) An extra meaningful parameter is introduced, resulting in the stronger classification generalization ability. Experimental results on both the artificial datasets and several benchmark datasets show that our IGEPSVM is superior to GEPSVM in both computation time and classification accuracy.

Keywords

eigenvalues and eigenfunctions; generalisation (artificial intelligence); optimisation; pattern classification; support vector machines; IGEPSVM; artificial datasets; benchmark datasets; classification generalization ability; improved generalized eigenvalue proximal support vector machine; optimization problem; pattern classification; standard eigenvalue decomposition; Accuracy; Benchmark testing; Educational institutions; Eigenvalues and eigenfunctions; Standards; Support vector machines; Training; Eigenvalue; pattern classification; proximal classification; support vector machine;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2012.2216874

Filename

6293857