A new Method for Fault‐Scarp Detection Using Linear Discriminant Analysis in High‐Resolution Bathymetry Data From the Alarcón Rise and Pescadero Basin

Tectonics ◽  
2021 ◽  
Author(s):  
L.A. Vega‐Ramírez ◽  
R.M. Spelz ◽  
R. Negrete‐Aranda ◽  
F. Neumann ◽  
D.W. Caress ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
High Resolution ◽  
Linear Discriminant Analysis ◽  
Fault Scarp ◽  
New Method ◽  
Linear Discriminant ◽  
High Resolution Bathymetry

Proposing a New Method Based on Linear Discriminant Analysis to Build a Robust Classifier

2014 ◽  
Vol 3 (3) ◽  
pp. 186-193
Author(s):  
Mohamad Iman Jamnejad ◽  
Hamid Parvin ◽  
Hamid Alinejad-Rokny ◽  
Ali Heidarzadegan
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
New Method ◽  
Linear Discriminant

Appropriateness of Linear Discriminant and Multinomial Classification Analysis in Marketing Research

1978 ◽  
Vol 15 (1) ◽  
pp. 103-112 ◽  
Author(s):  
William R. Dillon ◽  
Matthew Goldstein ◽  
Leon G. Schiffman
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Marketing Research ◽  
Classification Problem ◽  
Relative Performance ◽  
New Method ◽  
Classification Methods ◽  
Classification Analysis ◽  
Linear Discriminant ◽  
Usage Behavior

Buyer usage behavior data are used to compare the relative performance of a linear discriminant analysis and several multinomial classification methods. The potential shortcomings of each of the procedures investigated are cited, and a new method for determining the contribution of a variable to discrimination in the context of the multinomial classification problem also is presented.

Algorithm Study of Face Recognition on Improved 2DLDA

2011 ◽  
Vol 128-129 ◽  
pp. 58-61
Author(s):  
Shi Ping Li ◽  
Yu Cheng ◽  
Hui Bin Liu ◽  
Lin Mu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Small Sample ◽  
New Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Face Representation ◽  
Paired Class ◽  
The Relationship

Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction. To solve the “small sample” effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) [2] has been used for face recognition recently，but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method （N2DLDA） outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

Application of sparse linear discriminant analysis for metabolomics data

2014 ◽  
Vol 6 (22) ◽  
pp. 9037-9044 ◽  
Author(s):  
Meilan Ouyang ◽  
Zhimin Zhang ◽  
Chen Chen ◽  
Xinbo Liu ◽  
Yizeng Liang
Keyword(s):  
Discriminant Analysis ◽  
Variable Selection ◽  
Linear Discriminant Analysis ◽  
New Method ◽  
Metabolomics Data ◽  
Linear Discriminant

A new method performs classification and variable selection simultaneously to analyze complicated metabolomics datasets.

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