Complexity Selection with Cross-validation for Lasso and Sparse Partial Least Squares Using High-Dimensional Data

2013 ◽  
pp. 261-268 ◽  
Author(s):  
Anne-Laure Boulesteix ◽  
Adrian Richter ◽  
Christoph Bernau
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Cross Validation ◽  
High Dimensional Data ◽  
High Dimensional

Semi-Supervised Dimensionality Reduction based on Partial Least Squares for Visual Analysis of High Dimensional Data

2012 ◽  
Vol 31 (3pt4) ◽  
pp. 1345-1354 ◽  
Author(s):  
Jose Gustavo S. Paiva ◽  
William Robson Schwartz ◽  
Helio Pedrini ◽  
Rosane Minghim
Keyword(s):  
Dimensionality Reduction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Visual Analysis ◽  
High Dimensional Data ◽  
High Dimensional

Feature Selection Method Based on the Adaptive Genetic Algorithm-Kernel Partial Least Squares for High Dimensional Data

2012 ◽  
Vol 468-471 ◽  
pp. 1762-1766 ◽  
Author(s):  
Dong Yan ◽  
Shao Wei Liu ◽  
Jian Tang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
High Dimensional Data ◽  
Feature Selection Method ◽  
High Dimensional ◽  
Adaptive Genetic Algorithm ◽  
Kernel Partial Least Squares

Feature selection for modeling the high dimensional data, such as the near-infrared spectrum (NIR) is very important. A novel modeling approach combined the adaptive genetic algorithm-kernel partial least squares (AGA-KPLS) is proposed. The KPLS algorithm is used to construct nonlinear models with the popular kernel based modeling technology. The AGA is used to select the optimal feature sub-set from the original high dimensional data, which also used to select the kernel parameters of the KPLS algorithm simultaneously. The experimental results based on the vibration frequency spectrum show that the proposed approach has better prediction performance than the normal GA-PLS method.

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

The Analyst ◽  
2018 ◽  
Vol 143 (15) ◽  
pp. 3526-3539 ◽  
Author(s):  
Loong Chuen Lee ◽  
Choong-Yeun Liong ◽  
Abdul Aziz Jemain
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Knowledge Gaps ◽  
Contemporary Practice

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.

scholarly journals Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cicer Arietinum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Neutral Detergent Fiber ◽  
Acid Detergent Fiber ◽  
Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

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