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Compressor map regression modelling based on partial least squares [PDF]
Royal Society Open Science, 2018 In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN).
Xu Li +6 more
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Marginal Screening for Partial Least Squares Regression
IEEE Access, 2017 Partial least squares (PLS) regression is a versatile modeling approach for high-dimensional data analysis. Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability.
Naifei Zhao, Qingsong Xu, Hong Wang
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Partial Least Squares Enhances Genomic Prediction of New Environments
Frontiers in Genetics, 2022 In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as “leave one environment out,” is of paramount importance to increase the genetic gain in breeding programs and contribute to food
Osval A. Montesinos-López +8 more
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Completions of ε-Dense Partial Latin Squares [PDF]
, 2013 A classical question in combinatorics is the following: given a partial Latin square $P$, when can we complete $P$ to a Latin square $L$? In this paper, we investigate the class of textbf{$epsilon$-dense partial Latin squares}: partial Latin squares in
Bartlett, Padraic James +2 more
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Fault identification for chiller sensor based on partial least square method [PDF]
E3S Web of Conferences, 2021 Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component ...
Wu Bang +4 more
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Partial Least Squares Optimization Method Integrating Restricted Boltzmann Machine [PDF]
Jisuanji gongcheng, 2017 Partial Least Squares(PLS) method adopts Principal Component Analysis(PCA),it cannot express the nonlinear characteristic,and the prediction accuracy is low in the nonlinear data.Based on this,an analysis and predicting method combining Restricted ...
ZHU Zhipeng,DU Jianqiang,YU Riyue,NIE Bin
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Locality preserving partial least squares discriminant analysis for face recognition
Journal of King Saud University: Computer and Information Sciences, 2022 We propose a locality preserving partial least squares discriminant analysis (LPPLSDA) which adds a locality preserving feature to the conventional partial least squares discriminant analysis(PLS-DA).
Muhammad Aminu, Noor Atinah Ahmad
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Multi-trait genome prediction of new environments with partial least squares
Frontiers in Genetics, 2022 The genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a
Osval A. Montesinos-López +6 more
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Partial least-squares regression for soil salinity mapping in Bangladesh
Ecological Indicators, 2023 Estimating the salinity of the soil along the coast of south-western Bangladesh is the focus of this study. Thirteen soil salinity indicators were computed using the Landsat OLI images, and 241 soil salinity samples were gathered from secondary sources ...
Showmitra Kumar Sarkar +3 more
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Partial least squares regression in the social sciences [PDF]
Tutorials in Quantitative Methods for Psychology, 2015 Partial least square regression (PLSR) is a statistical modeling technique that extracts latent factors to explain both predictor and response variation.
Megan L. Sawatsky +2 more
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