Quadratic and Convex Minimax Classification problems

被引:3
|
作者
Kitahara, Tomonari [1 ]
Mizuno, Shinji [1 ]
Nakata, Kazuhide [1 ]
机构
[1] Tokyo Inst Technol, Dept Ind Engn & Management, Meguro Ku, Tokyo 1528552, Japan
基金
日本学术振兴会;
关键词
optimization; minimax classification; semidefinite programming; convexity;
D O I
10.15807/jorsj.51.191
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
When there are two classes whose mean vectors and covariance matrices axe known, Lanckriet et al. [7] consider the Linear Minimax Classification (LMC) problem and they propose a method for solving it. In this paper we first discuss the Quadratic Minimax Classification (QMC) problem, which is a generalization of LMC. We show that QMC is transformed to a parametric Semidefinite Programming (SDP) problem. We further define the Convex Minimax Classification (CMC) problem. Though the two problems are generalizations of UMC, we prove that solutions of these problems can be obtained by solving LMC.
引用
收藏
页码:191 / 201
页数:11
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