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Advanced Science, EarlyView.Long‐Tea‐CLIP (Contrastive Language‐Image Pre‐training) presents a multimodal AI framework that integrates visual, metabolomic, and sensory knowledge to grade green tea across appearance, soup color, aroma, taste, and infused leaf. By combining expert‐guided modeling with CLIP‐supervised learning, the system delivers fine‐grained quality evaluation and
Yanqun Xu +9 more
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Partial least-squares regression: a tutorial
Analytica Chimica Acta, 1986Abstract A tutorial on the partial least-squares (PLS) regression method is provided. Weak points in some other regression methods are outlined and PLS is developed as a remedy for those weaknesses. An algorithm for a predictive PLS and some practical hints for its use are given.
Paul Geladi, Bruce R. Kowalski
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Robust methods for partial least squares regression
Journal of Chemometrics, 2003AbstractPartial least squares regression (PLSR) is a linear regression technique developed to deal with high‐dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm, this being the leading PLSR algorithm because of its speed and efficiency.
M. Hubert, K. Vanden Branden
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Partial least trimmed squares regression
Chemometrics and Intelligent Laboratory Systems, 2022Zhonghao Xie, Xi'an Feng, Xiaojing Chen
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Partial least median of squares regression
Journal of Chemometrics, 2022Abstract In modern data analysis, there is an increasing availability of datasets with numerous variables. Linear models that deal with abundant predictor variables often have poor performance because they tend to produce large variances. As well known, partial least squares (PLS) regression standouts because it is serviceable even if
Zhonghao Xie +3 more
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Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
2012Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
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Domain-Invariant Partial-Least-Squares Regression
Analytical Chemistry, 2018Multivariate 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 ...
Ramin Nikzad-Langerodi +3 more
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Partial Least‐Squares Regression
2013This chapter presents the most widely applied and, probably, satisfactory multivariate regression method used nowadays: partial least squares (PLS). Graphical explanations of many concepts are given to complement the more formal mathematical background. Several approaches to solving current problems are suggested.
José Manuel Andrade‐Garda +3 more
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Kernel Partial Least-Squares Regression
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical ...
null Bai Yifeng +2 more
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