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MKPLS approach: switching strategies for the non-linear multi-kernel PLSR
Computational Statistics, 2007A multi-kernel algorithm is proposed for multilinear partial least squares regression in which one kernel matrix is used for the number of first factors and another matrix is applied to the remaining ones. Kernel switching strategies based on the PRESS criterion are discussed. Results of nearly infrared spectral analysis are presented.
Raúl P. Rentería +2 more
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Fast and high accuracy classification of sleep EEG using PLSR method
2013 21st Signal Processing and Communications Applications Conference (SIU), 2013The aim of this study is to classify the status of sleep from electroencefelography (EEG) data recorded from seven different healthy individuals. The twenty two autoregressive (AR) model coefficent are computed and used as features. Three classification algorithms, namely k-NN, Bayes and PLSR methods are trained and tested.
Kübra Eroglu +2 more
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Multivariate image regression (MIR): implementation of image PLSR?first forays
Journal of Chemometrics, 2000In large and important sectors of modern production, there is an increased demand for on-line or at-line information. Both consumers and governmental regulations require that producers can document the quality of their products. The more precise measurements can be, the more potentially valuable they are for the producers in their endeavours to fulfil ...
Lied, T. T. +2 more
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Multivariate chaotic time series prediction based on PLSR and MKELM
2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP), 2015This paper presents a method based on partial least squares regression (PLSR) and multiple kernel extreme learning machine (MKLEM) for multivariate chaotic time series prediction. At first, singular spectrum analysis (SSA) is applied for the time series extraction of complex trends and eliminating the influence of noise.
Meiling Xu, Ruiquan Zhang, Min Han
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Prediction of blackwood Kraft pulps yields with wood NIR–PLSR models
Wood Science and Technology, 2016Pulp yield is an important measure of pulpwood quality, which is used regularly by the pulp and paper industry for which the possibility of using rapid methods to predict pulp yield would be very useful for screening and quality control. This work addresses the prediction of Kraft pulp yield under standard identical conditions and targeted to a kappa ...
Santos, António J. +2 more
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A local modeling approach of craniofacial shape correlation based on PLSR
2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2017Statistical craniofacial reconstruction methods have become an important research aspect in computer-aided craniofacial reconstruction. The existing joint statistical craniofacial reconstruction method based on PCA modeling is not scientific in terms of forensic anthropology, and its description ability for the shape correlation of skull and face is ...
Yiyue He +3 more
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PLSR: Unstructured Pruning with Layer-Wise Sparsity Ratio
2023 International Conference on Machine Learning and Applications (ICMLA), 2023Haocheng Zhao +5 more
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Application of GPA and PLSR in correlating sensory and chemical data sets
Food Quality and Preference, 2003Abstract This paper discusses the application of various multivariate statistical procedures to understand the relationship between sensory and instrumental flavor profiles. Ice cream with varying fat levels was used as the vehicle for the flavor compounds in the experiment.
Seo-Jin Chung +2 more
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Wavelength Selection to Predict Crude Protein in Sorghum using PLSR
2018 Asia Communications and Photonics Conference (ACP), 2018We have applied PLSR on NIRS data of Sorghum to select significant wavelengths for Crude-Protein content. Calibration models proposed using single wavelength (1449nm) and a pair (1485nm and 1486nm) give the coefficient of determination (R2) as 0.7928 and 0.8334 respectively.
Nikita Chaturvedi +4 more
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