Asymptotic comparison of semi-supervised and supervised linear discriminant functions for heteroscedastic normal populations

研究成果: Article査読

1 被引用数 (Scopus)

抄録

It has been reported that using unlabeled data together with labeled data to construct a discriminant function works successfully in practice. However, theoretical studies have implied that unlabeled data can sometimes adversely affect the performance of discriminant functions. Therefore, it is important to know what situations call for the use of unlabeled data. In this paper, asymptotic relative efficiency is presented as the measure for comparing analyses with and without unlabeled data under the heteroscedastic normality assumption. The linear discriminant function maximizing the area under the receiver operating characteristic curve is considered. Asymptotic relative efficiency is evaluated to investigate when and how unlabeled data contribute to improving discriminant performance under several conditions. The results show that asymptotic relative efficiency depends mainly on the heteroscedasticity of the covariance matrices and the stochastic structure of observing the labels of the cases.

本文言語English
ページ(範囲)215-339
ページ数125
ジャーナルAdvances in Data Analysis and Classification
12
2
DOI
出版ステータスPublished - 2018 7 27

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 応用数学

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