Covariance-enhanced discriminant analysis

• 成果类型: Article
• 署名作者: Xu, Peirong; Zhu, Ji; Zhu, Lixing; Li, Yi
• 署名单位: Southeast University - China; University of Michigan System; University of Michigan; Hong Kong Baptist University; University of Michigan System; University of Michigan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu049
• 发表日期: 2015
• 页码: 3345
• 关键词: VARIABLE SELECTION; shrunken centroids; CLASSIFICATION; protein; expression; regression; MODEL

摘要

Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial.