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.

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.

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