Results 11 to 20 of about 181,998 (262)
Integrative sparse partial least squares [PDF]
Statistics in Medicine, 2021 Partial least squares, as a dimension reduction technique, has become increasingly important for its ability to deal with problems with a large number of variables. Since noisy variables may weaken estimation performance, the sparse partial least squares (SPLS) technique has been proposed to identify important variables and generate more interpretable ...
Weijuan Liang +3 more
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Journal of Multivariate Analysis, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bousebata, Meryem +2 more
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Bayesian Sparse Partial Least Squares [PDF]
Neural Computation, 2013 Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years.
Diego Vidaurre +4 more
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Partial least squares for face hashing [PDF]
Neurocomputing, 2016 Face identification is an important research topic due to areas such as its application to surveillance, forensics and human-computer interaction. In the past few years, a myriad of methods for face identification has been proposed in the literature, with just a few among them focusing on scalability.
dos Santos, Cassio +3 more
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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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Penerapan Partial Least Squares Pada Data Gingerol
ComTech, 2010 Multivariate calibration model aims to predict the expensive measures obtained by using the measures of a cheap and easy. There are several problems that often occur in the model calibration, among others, and multikolinear. To overcome these problems we
Margaretha Ohyver
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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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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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PLASMA: Partial LeAst Squares for Multiomics Analysis. [PDF]
Cancers (Basel), 2023 Background/Objectives: Recent growth in the number and applications of high-throughput “omics” technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. Methods: Here we present the first algorithm, PLASMA, that can learn to
Yamaguchi K +5 more
europepmc +3 more sources