Significant vector learning to construct sparse kernel regression models

被引：22
作者：
Gao, Junbin [1 ]
Shi, Daming
Liu, Xiaomao
机构：
[1] Charles Sturt Univ, Sch Comp Sci, Bathurst, NSW 2795, Australia
[2] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore
[3] Huazhong Univ Sci & Technol, Dept Math, Wuhan 430074, Peoples R China
来源：
NEURAL NETWORKS | 2007年 / 20卷 / 07期
基金：
中国国家自然科学基金;
关键词：
sparse kernel regression; relevance vector machine; orthogonal least square; significant vector machine;
D O I：
10.1016/j.neunet.2007.03.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel significant vector (SV) regression algorithm is proposed in this paper based on an analysis of Chen's orthogonal least squares (OLS) regression algorithm. The proposed regularized SV algorithm finds the significant vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing the orthogonalization needed in the OLS algorithm. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：791 / 798
页数：8
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