A flexible and novel strategy of alternating trilinear decomposition method coupled with two-dimensional linear discriminant analysis for three-way chemical data analysis: Characterization and classification

2018 ◽  
Vol 1021 ◽  
pp. 28-40 ◽  
Author(s):  
Yong Hu ◽  
Hai-Long Wu ◽  
Xiao-Li Yin ◽  
Hui-Wen Gu ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Decomposition Method ◽  
Chemical Data ◽  
Two Dimensional ◽  
Linear Discriminant ◽  
Trilinear Decomposition ◽  
Alternating Trilinear Decomposition ◽  
Novel Strategy

Two-dimensional linear discriminant analysis for classification of three-way chemical data

2016 ◽  
Vol 938 ◽  
pp. 53-62 ◽  
Author(s):  
Adenilton C. da Silva ◽  
Sófacles F.C. Soares ◽  
Matías Insausti ◽  
Roberto K.H. Galvão ◽  
Beatriz S.F. Band ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Chemical Data ◽  
Two Dimensional ◽  
Linear Discriminant

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

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