Principal Discriminant Variate Method for Classification of Multicollinear Data: Applications to Near-Infrared Spectra of Cow Blood Samples
Principal Discriminant Variate Method for Classification of Multicollinear Data: Applications to Near-Infrared Spectra of Cow Blood Samples
复制标题
多重共线性数据分类的主判别变量法：在牛血样本近红外光谱中的应用
DOI：
10.1366/0003702021954944
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发表时间：
2002
影响因子：
3.5
通讯作者：
Y. Ozaki
中科院分区：
文献类型：
--
作者：
Jian;R. Tsenkova;Yuqing Wu;R. Yu;Y. Ozaki
提出了一种新的正则化判别分析技术--主判别变量(PDV)方法，以有效地处理多变量光谱分类中常见的多线性数据。这种方法背后的动机是寻找一系列判别方向，这些方向不仅优化不同类别之间的可分性，而且考虑到数据中存在的最大变化。这一动机为PDV方法提供了更好的预测稳定性，而不会显著损失可分离性。给出了PDV方法的不同形式，并提出了一种有效的PDV方法计算方法。两组近红外(NIR)光谱数据，一组对应于两头奶牛的血浆样本，另一组对应于乳房和健康奶牛的全血样本，被用来评估PDV方法的行为，并与主成分分析(PCA)、判别偏最小二乘(DPLS)、软独立类比建模(SIMCA)和Fisher线性判别分析(FLDA)进行比较。结果表明，PDV方法能很好地区分不同类别血浆样品的近红外光谱，其性能优于主成分分析、DPLS、SIMCA和FLDA，表明PDV方法在成分差异很小的光谱特征样品的判别分析中是一种有前途的工具。
A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for effectively handling multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. This motivation furnishes the PDV method with improved stability in prediction without significant loss of separability. Different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Two sets of near-infrared (NIR) spectra data, one corresponding to the blood plasma samples from two cows and the other associated with the whole blood samples from mastitic and healthy cows, have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least-squares (DPLS), soft independent modeling of class analogies (SIMCA), and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the NIR spectra of blood plasma samples from different classes are clearly discriminated by the PDV method, and the proposed method provides superior performance to PCA, DPLS, SIMCA, and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional differences.