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首页> 外文期刊>Journal of Chemometrics >An optimization-based undeflated PLS (OUPLS)method to handlemissing data in the training set
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An optimization-based undeflated PLS (OUPLS)method to handlemissing data in the training set

机译：基于优化的未压缩PLS（OUPLS）方法来处理训练集中的数据丢失

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Advances in sensory systems have led to many industrial applications with large amounts of highly correlated data, particularly in chemical and pharmaceutical processes.With these correlated data sets, it becomes important to consider advanced modeling approaches built to deal with correlated inputs in order to understand the underlying sources of variability and how this variability will affect the final quality of the product. Additional to the correlated nature of the data sets, it is also common to find missing elements and noise in these data matrices. Latent variable regression methods such as partial least squares or projection to latent structures (PLS) have gained much attention in industry for their ability to handle ill-conditioned matrices with missing elements. This feature of the PLS method is accomplished through the nonlinear iterative PLS (NIPALS) algorithm, with a simple modification to consider the missing data. Moreover, in expectation maximization PLS (EM-PLS), imputed values are provided for missing data elements as initial estimates, conventional PLS is then applied to update these elements, and the process iterates to convergence. This study is the extension of previous work for principal component analysis (PCA), where we introduced nonlinear programming (NLP) as a means to estimate the parameters of the PCA model. Here, we focus on the parameters of a PLS model. As an alternative tomodified NIPALS and EM-PLS, this paper presents an efficient NLP-based technique to find model parameters for PLS, where the desired properties of the parameters can be explicitly posed as constraints in the optimization problem of the proposed algorithm. We also present a number of simulation studies, where we compare effectiveness of the proposed algorithm with competing algorithms.
机译：感官系统的进步已导致许多工业应用获得大量高度相关的数据，特别是在化学和制药过程中。有了这些相关的数据集，考虑构建用于处理相关输入的高级建模方法以了解其重要性就变得很重要。潜在的可变性来源以及这种可变性将如何影响产品的最终质量。除了数据集的相关性质外，在这些数据矩阵中查找丢失的元素和噪声也很常见。诸如局部最小二乘或潜在结构投影（PLS）之类的潜在变量回归方法因其能够处理缺少元素的病态矩阵而备受关注。 PLS方法的此功能是通过非线性迭代PLS（NIPALS）算法实现的，并进行了简单修改以考虑丢失的数据。此外，在期望最大化PLS（EM-PLS）中，为丢失的数据元素提供了估算值作为初始估计，然后应用常规PLS更新这些元素，并且过程迭代到收敛。这项研究是对主成分分析（PCA）先前工作的扩展，在此我们引入了非线性规划（NLP）作为估算PCA模型参数的一种方法。在这里，我们重点介绍PLS模型的参数。作为修改后的NIPALS和EM-PLS的替代方法，本文提出了一种基于NLP的高效技术来查找PLS的模型参数，其中，所需参数的属性可以明确地作为提出算法的优化问题中的约束。我们还提出了许多仿真研究，在这些研究中，我们比较了所提出算法与竞争算法的有效性。

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