OPLS discriminant analysis:: combining the strengths of PLS-DA and SIMCA classification
OPLS discriminant analysis:: combining the strengths of PLS-DA and SIMCA classification
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DOI：
10.1002/cem.1006
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发表时间：
2006-08-01
影响因子：
2.4
通讯作者：
Trygg, Johan
中科院分区：
文献类型：
--
作者：
Bylesjo, Max;Rantalainen, Mattias;Trygg, Johan
研究了OPLS方法的判别分析特性。我们演示了如何利用类正交变化来增强分类性能的情况下，个别类表现出分歧的类内变化，在类比软独立建模类类比（SIMCA）分类。如果类中不存在这种变化，则预测结果将在很大程度上等同于使用PLS-DA的传统监督分类。因此，一个歧视性的战略概述，结合优势的PLS-DA和SIMCA分类的框架内的OPLS-DA方法。此外，已被采用的方法来生成预测的分类结果的分布，并随后评估分类信念。这使得能够在适当的统计背景下利用类正交变化。建议的决策规则进行比较，共同的决策规则，并产生可比的或更少的类偏置的分类结果。版权所有（c）2007约翰威利父子有限公司。
The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results. Copyright (c) 2007 John Wiley & Sons, Ltd.