Optimal locality preserving least square support vector machine

doi:10.1007/s11460-011-0138-y

Front Elect Electr Eng Chin

0, Vol.

Issue () : 201-207 https://doi.org/10.1007/s11460-011-0138-y

RESEARCH ARTICLE

Optimal locality preserving least square support vector machine

Xiaobo CHEN^1,2, Jian YANG¹(

)

1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China; 2. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China

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Abstract

In this paper, a novel least square support vector machine (LSSVM), termed as optimal locality preserving LSSVM (OLP-LSSVM) is proposed. By integrating structural risk minimization and locality preserving criterion in a unified framework, the resulting separating hyperplane is not only in accordance with the structural risk minimization principle but also be sensitive to the manifold structure of data points. The proposed model can be solved efficiently by alternating optimization method. Experimental results on several public available benchmark datasets show the feasibility and effectiveness of the proposed method.

Keywords structural risk minimization locality preserving criterion support vector machine (SVM) least square classifier

Corresponding Author(s): YANG Jian,Email:csjyang@mail.njust.edu.cn

Issue Date: 05 June 2011

Cite this article:

Xiaobo CHEN,Jian YANG. Optimal locality preserving least square support vector machine[J]. Front Elect Electr Eng Chin, 0, (): 201-207.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-011-0138-y
https://academic.hep.com.cn/fee/EN/Y0/V/I/201

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