Development of a classification and ranking method for the identification of possible biomarkers in two-dimensional gel-electrophoresis based on principal component analysis and variable selection procedures

2011 ◽  
Vol 7 (3) ◽  
pp. 677 ◽  
Author(s):  
Elisa Robotti ◽  
Marco Demartini ◽  
Fabio Gosetti ◽  
Giorgio Calabrese ◽  
Emilio Marengo
Keyword(s):  
Principal Component Analysis ◽  
Variable Selection ◽  
Gel Electrophoresis ◽  
Principal Component ◽  
Component Analysis ◽  
Ranking Method ◽  
Two Dimensional ◽  
Two Dimensional Gel Electrophoresis ◽  
Selection Procedures

Exact methods for variable selection in principal component analysis: Guide functions and pre-selection

2013 ◽  
Vol 57 (1) ◽  
pp. 95-111 ◽  
Author(s):  
Joaquín Pacheco ◽  
Silvia Casado ◽  
Santiago Porras
Keyword(s):  
Principal Component Analysis ◽  
Variable Selection ◽  
Principal Component ◽  
Component Analysis ◽  
Exact Methods

Optimal mean two-dimensional principal component analysis with F-norm minimization

2017 ◽  
Vol 68 ◽  
pp. 286-294 ◽  
Author(s):  
Qianqian Wang ◽  
Quanxue Gao ◽  
Xinbo Gao ◽  
Feiping Nie
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Norm Minimization

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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