Variations of Large Spectral Sets; Two-Dimensional Correlation Analysis of Loadings Spectra of Principal Component Analysis

2003 ◽  
Vol 11 (6) ◽  
pp. 457-466 ◽  
Author(s):  
Yongliang Liu ◽  
Franklin E. Barton ◽  
Brenda G. Lyon ◽  
Yud-Ren Chen
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Spectral Sets

scholarly journals New Approach to Generalized Two-Dimensional Correlation Spectroscopy. 1: Combination of Principal Component Analysis and Two-Dimensional Correlation Spectroscopy

2002 ◽  
Vol 56 (12) ◽  
pp. 1562-1567 ◽  
Author(s):  
Young Mee Jung ◽  
Hyeon Suk Shin ◽  
Seung Bin Kim ◽  
Isao Noda
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Correlation Spectroscopy ◽  
Data Matrix ◽  
Two Dimensional ◽  
Noise Component ◽  
Reconstructed Data ◽  
2D Correlation Analysis

The direct combination of chemometrics and two-dimensional (2D) correlation spectroscopy is considered. The use of a reconstructed data matrix based on the significant scores and loading vectors obtained from the principal component analysis (PCA) of raw spectral data is proposed as a method to improve the data quality for 2D correlation analysis. The synthetic noisy spectra were analyzed to explore the novel possibility of the use of PCA-reconstructed spectra, which are highly noise suppressed. 2D correlation analysis of this reconstructed data matrix, instead of the raw data matrix, can significantly reduce the contribution of the noise component to the resulting 2D correlation spectra.

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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