島根大学総合理工学部紀要.シリーズB

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島根大学総合理工学部紀要.シリーズB 48
2015-03 発行

Asymptotic theory for discriminant analysis in high dimension low sample size

玉谷 充
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内容記述(抄録等)
This paper is based on the author's thesis, “Pattern recognition based on naive canonical correlations in high dimension low sample size”. This paper is concerned with discriminant analysis for multi-class problems in a High Dimension Low Sample Size (hdlss) context. The proposed discrimination method is based on canonical correlations between the predictors and response vector of class label. We investigate the asymptotic behavior of the discrimination method, and evaluate bounds for its misclassication rate.