Linear discriminant analysis, partial least squares discriminant analysis, and soft independent modeling of class analogy of experimental and simulated near-infrared spectra of a cultivation medium for mammalian cells

2018 ◽  
Vol 32 (4) ◽  
pp. e3005 ◽  
Author(s):  
Éva Szabó ◽  
Szilveszter Gergely ◽  
András Salgó
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Mammalian Cells ◽  
Near Infrared ◽  
Cultivation Medium ◽  
Linear Discriminant ◽  
Near Infrared Spectra

An improved ensemble partial least squares for analysis of near-infrared spectra

Talanta ◽  
2012 ◽  
Vol 94 ◽  
pp. 301-307 ◽  
Author(s):  
Yong Hu ◽  
Silong Peng ◽  
Jiangtao Peng ◽  
Jiping Wei
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra

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.

Dual stacked partial least squares for analysis of near-infrared spectra

2013 ◽  
Vol 792 ◽  
pp. 19-27 ◽  
Author(s):  
Yiming Bi ◽  
Qiong Xie ◽  
Silong Peng ◽  
Liang Tang ◽  
Yong Hu ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra
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