Quadratic and convex minimax classification problems

Tomonari Kitahara, Shinji Mizuno, Kazuhide Nakata

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

When there are two classes whose mean vectors and covariance matrices are 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 LMC, we prove that solutions of these problems can be obtained by solving LMC.

Original languageEnglish
Pages (from-to)191-201
Number of pages11
JournalJournal of the Operations Research Society of Japan
Volume51
Issue number2
DOIs
Publication statusPublished - Jun 2008
Externally publishedYes

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Minimax
Semidefinite programming
Covariance matrix

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

Cite this

Quadratic and convex minimax classification problems. / Kitahara, Tomonari; Mizuno, Shinji; Nakata, Kazuhide.

In: Journal of the Operations Research Society of Japan, Vol. 51, No. 2, 06.2008, p. 191-201.

Research output: Contribution to journalArticle

Kitahara, Tomonari ; Mizuno, Shinji ; Nakata, Kazuhide. / Quadratic and convex minimax classification problems. In: Journal of the Operations Research Society of Japan. 2008 ; Vol. 51, No. 2. pp. 191-201.
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