Online algorithm based on support vectors for orthogonal regression

被引：13
|
作者：
Souza, Roberto C. S. N. P. [1 ]
Leite, Saul C. [1 ]
Borges, Carlos C. H. [1 ]
Neto, Raul Fonseca [1 ]
机构：
[1] Univ Fed Juiz de Fora, Dept Comp Sci, Juiz De Fora, MG, Brazil
来源：
PATTERN RECOGNITION LETTERS | 2013年 / 34卷 / 12期
关键词：
Support vector machines; Online algorithms; Kernel methods; Regression problem; Orthogonal regression; TOTAL LEAST-SQUARES; MODEL;
D O I：
10.1016/j.patrec.2013.04.023
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we introduce a new online algorithm for orthogonal regression. The method is constructed via an stochastic gradient descent approach combined with the idea of a tube loss function, which is similar to the one used in support vector (SV) regression. The algorithm can be used in primal or in dual variables. The latter formulation allows the introduction of kernels and soft margins. In addition, an incremental strategy algorithm is introduced, which can be used to find sparse solutions and also an approximation to the "minimal tube" containing the data. The algorithm is very simple to implement and avoids quadratic optimization. (c) 2013 Published by Elsevier B.V.
引用
收藏
页码：1394 / 1404
页数：11
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