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Bias‐Corrected Diagonal Discriminant Rules for High‐Dimensional Classification
Author(s) -
Huang Song,
Tong Tiejun,
Zhao Hongyu
Publication year - 2010
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01395.x
Subject(s) - discriminant , linear discriminant analysis , diagonal , optimal discriminant analysis , discriminant function analysis , quadratic classifier , pattern recognition (psychology) , mathematics , artificial intelligence , quadratic equation , function (biology) , statistics , computer science , support vector machine , evolutionary biology , biology , geometry
Summary Diagonal discriminant rules have been successfully used for high‐dimensional classification problems, but suffer from the serious drawback of biased discriminant scores. In this article, we propose improved diagonal discriminant rules with bias‐corrected discriminant scores for high‐dimensional classification. We show that the proposed discriminant scores dominate the standard ones under the quadratic loss function. Analytical results on why the bias‐corrected rules can potentially improve the predication accuracy are also provided. Finally, we demonstrate the improvement of the proposed rules over the original ones through extensive simulation studies and real case studies.