Finite-sample analysis of impacts of unlabeled data and their labeling mechanisms in linear discriminant analysis

Kenichi Hayashi, Keiji Takai

研究成果: Article査読

1 被引用数 (Scopus)

抄録

It is widely believed that unlabeled data are promising for improving prediction accuracy in classification problems. Although theoretical studies about when/how unlabeled data are beneficial exist, an actual prediction improvement has not been sufficiently investigated for a finite sample in a systematic manner. We investigate the impact of unlabeled data in linear discriminant analysis and compare the error rates of the classifiers estimated with/without unlabeled data. Our focus is a labeling mechanism that characterizes the probabilistic structure of occurrence of labeled cases. Results imply that an extremely small proportion of unlabeled data has a large effect on the analysis results.

本文言語English
ページ(範囲)184-203
ページ数20
ジャーナルCommunications in Statistics: Simulation and Computation
46
1
DOI
出版ステータスPublished - 2017 1 2
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • モデリングとシミュレーション

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