Covariance-enhanced discriminant analysis.
Covariance-enhanced discriminant analysis.
复制标题
协方差增强判别分析。
作者：
Xu P;Zhu JI;Zhu L;Li YI
线性判别分析已被广泛用于通过特征的线性组合来描述或分离多个类别。然而，现代生物学实验的高维特征违背了传统的判别分析技术。可能的特征间相关性带来了额外的挑战，并且在建模中经常未得到充分利用。本文通过结合可能的特征间相关性，提出了一种协方差增强的判别分析方法，该方法可以同时一致地选择信息特征并识别相应的可判别类。在温和的正则性条件下，我们证明了该方法可以实现一致的参数估计和模型选择，并且可以获得渐近最优的误分类率。大量的模拟验证了该方法的实用性，我们将其应用于肾移植试验。
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.