Two Types of Partial Least Squares Method in Linear Discriminant Analysis

1998 ◽  
pp. 261-267
Author(s):  
Hyun Bin Kim ◽  
Yutaka Tanaka
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Linear Discriminant ◽  
Partial Least Squares Method

scholarly journals PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification Problem

2020 ◽  
Vol 43 (2) ◽  
pp. 233-249
Author(s):  
Adolphus Wagala ◽  
Graciela González-Farías ◽  
Rogelio Ramos ◽  
Oscar Dalmau
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Regression ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Classification Error ◽  
Support Vector ◽  
Linear Discriminant ◽  
Generalized Linear Regression

This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining  it with logistic regression and  linear  discriminant analysis,  to  get a  partial least  squares generalized linear  regression-logistic regression model (PLSGLR-log),  and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative  study  of  the obtained  classifiers with   the   classical  methodologies like  the k-nearest  neighbours (KNN), linear   discriminant  analysis  (LDA),   partial  least  squares discriminant analysis (PLSDA),  ridge  partial least squares (RPLS), and  support vector machines(SVM)  is  then  carried  out.    Furthermore,  a  new  methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based  on the lowest  classification error  rates  compared to  the  others  when  applied   to  the  types   of data   are considered;  the  un- preprocessed and preprocessed.

Face and Ethnical Group Recognition with Images of Different Resolutions

2021 ◽  
Vol 9 (1) ◽  
pp. 140-147
Author(s):  
Chong Lu ◽  
Yan Ren ◽  
Liying Han
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Two Dimensional ◽  
Face Database ◽  
Linear Discriminant ◽  
Better Than

In this paper, a dataset for Xinjiang minority ethnical groups is introduced, and implementation of two dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Partial Least Squares (2DPLS) is investigated. Two important topics for face recognition and the ethnicity recognition are investigated for database with different image resolutions. Experiments show that 2DLDA performances better than 2DPLS on our face database.

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