Iterative re-weighted multilinear partial least squares modelling for robust predictive modelling

IF 2.3 4区 化学 Q1 SOCIAL WORK Journal of Chemometrics Pub Date : 2023-12-06 DOI:10.1002/cem.3527
Puneet Mishra, Kristian Hovde Liland
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Abstract

Higher order data are commonly encountered in the domain of chemometrics, often generated by advanced analytical instruments. Due to the multilinear nature of the data, higher order data require different regression approaches compared with traditional two-way data for predictive modelling. The main aim is usually to extract the rich multilinear information, which is often lost if the data are simply analysed in unfolded form. A common algorithm for multilinear predictive modelling is N-way partial least squares (NPLS). However, a limitation of NPLS is that it inherently does not handle outlying observations, which can be detrimental to the model. This work presents a new robust multilinear predictive modelling approach based on iterative down-weighting of the outlier observations in both predictor and response space. A key benefit of the method is that it only requires a single extra parameter to tune. In this work, the algorithm is described, and the method is demonstrated on three real multilinear data sets. In all cases, the presented method outperformed the traditional NPLS modelling regarding the root mean squared error of prediction.

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鲁棒预测模型的迭代重加权多元线性偏最小二乘建模
高阶数据通常在化学计量学领域中遇到,通常由先进的分析仪器产生。由于数据的多线性性质,与传统的双向数据相比,高阶数据需要不同的回归方法来进行预测建模。主要目的通常是提取丰富的多线性信息,如果简单地以展开形式分析数据,这些信息往往会丢失。多线性预测建模的常用算法是n向偏最小二乘法。然而,不良贷款的一个限制是它本质上不处理外围观测,这可能对模型有害。这项工作提出了一种新的鲁棒多线性预测建模方法,该方法基于预测和响应空间中离群值观测的迭代降权。该方法的一个关键优点是,它只需要一个额外的参数即可进行调优。本文对该算法进行了描述,并在三个真实的多线性数据集上进行了验证。在所有情况下,所提出的方法在预测均方根误差方面优于传统的不良贷款建模。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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