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Domain-Invariant Partial-Least-Squares Regression
Analytical Chemistry ( IF 6.7 ) Pub Date : 2018-05-03 00:00:00 , DOI: 10.1021/acs.analchem.8b00498
Ramin Nikzad-Langerodi 1 , Werner Zellinger 1 , Edwin Lughofer 1 , Susanne Saminger-Platz 1
Analytical Chemistry ( IF 6.7 ) Pub Date : 2018-05-03 00:00:00 , DOI: 10.1021/acs.analchem.8b00498
Ramin Nikzad-Langerodi 1 , Werner Zellinger 1 , Edwin Lughofer 1 , Susanne Saminger-Platz 1
Affiliation
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Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devices, while generic methods for calibration-model adaptation are largely missing. To fill this gap, we here introduce domain-invariant partial-least-squares (di-PLS) regression, which extends ordinary PLS by a domain regularizer in order to align the source and target distributions in the latent-variable space. We show that a domain-invariant weight vector can be derived in closed form, which allows the integration of (partially) labeled data from the source and target domains as well as entirely unlabeled data from the latter. We test our approach on a simulated data set where the aim is to desensitize a source calibration model to an unknown interfering agent in the target domain (i.e., unsupervised model adaptation). In addition, we demonstrate unsupervised, semisupervised, and supervised model adaptation by di-PLS on two real-world near-infrared (NIR) spectroscopic data sets.
中文翻译:
领域不变的偏最小二乘回归
由于与仪器响应,环境条件或样品基质相关的变化,多元校准模型通常无法外推到校准样品之外。当前用于使源校准模型适应目标域的大多数方法仅适用于相似分析设备之间的校准转移,而很大程度上缺少用于校准模型适应的通用方法。为了填补这一空白,我们在这里引入了领域不变的偏最小二乘(di-PLS)回归,该回归通过领域正则化器扩展了普通PLS,以对齐潜在变量空间中的源分布和目标分布。我们证明了域不变权重向量可以以封闭形式导出,这样就可以集成(部分)来自源域和目标域的标记数据,以及来自源域和目标域的完全未标记的数据。我们在模拟数据集上测试我们的方法,其目的是使源校准模型对目标域中的未知干扰剂不敏感(即,无监督模型适应)。此外,我们在两个真实世界的近红外(NIR)光谱数据集上展示了di-PLS进行的无监督,半监督和监督模型自适应。
更新日期:2018-05-03
中文翻译:
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领域不变的偏最小二乘回归
由于与仪器响应,环境条件或样品基质相关的变化,多元校准模型通常无法外推到校准样品之外。当前用于使源校准模型适应目标域的大多数方法仅适用于相似分析设备之间的校准转移,而很大程度上缺少用于校准模型适应的通用方法。为了填补这一空白,我们在这里引入了领域不变的偏最小二乘(di-PLS)回归,该回归通过领域正则化器扩展了普通PLS,以对齐潜在变量空间中的源分布和目标分布。我们证明了域不变权重向量可以以封闭形式导出,这样就可以集成(部分)来自源域和目标域的标记数据,以及来自源域和目标域的完全未标记的数据。我们在模拟数据集上测试我们的方法,其目的是使源校准模型对目标域中的未知干扰剂不敏感(即,无监督模型适应)。此外,我们在两个真实世界的近红外(NIR)光谱数据集上展示了di-PLS进行的无监督,半监督和监督模型自适应。