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Constrained Principal Component Analysis and Related Techniques

Publisher: 
Chapman & Hall/CRC
Number of Pages: 
233
Price: 
89.95
ISBN: 
9781466556669
Date Received: 
Wednesday, November 13, 2013
Reviewable: 
No
Include In BLL Rating: 
No
Yoshio Takane
Series: 
Monographs on Statistics and Applied Probability 129
Publication Date: 
2013
Format: 
Hardcover
Category: 
Monograph
Tags: 

Introduction
Analysis of Mezzich’s Data
Analysis of Food and Cancer Data
Analysis of Greenacre’s Data
Analysis of Tocher’s Data
A Summary of the Analyses in This Chapter

Mathematical Foundation
Preliminaries
Projection Matrices
Singular Value Decomposition (SVD)

Constrained Principal Component Analysis (CPCA)
Data Requirements
CPCA: Method
Generalizations

Special Cases and Related Methods
Pre- and Postprocessings
Redundancy Analysis (RA)
Canonical Correlation Analysis (CANO)
Canonical Discriminant Analysis (CDA)
Multidimensional Scaling (MDS)
Correspondence Analysis (CA)
Constrained CA
Nonsymmetric CA (NSCA)
Multiple-Set CANO (GCANO)
Multiple Correspondence Analysis (MCA)
Vector Preference Models
Two-Way CANDELINC
Growth Curve Models (GCM)
Extended Growth Curve Models (ExGCM)
Seemingly Unrelated Regression (SUR)
Wedderburn–Guttman Decomposition
Multilevel RA (MLRA)
Weighted Low Rank Approximations (WLRA)
Orthogonal Procrustes Rotation
PCA of Image Data Matrices

Related Topics of Interest
Dimensionality Selection
Reliability Assessment
Determining the Value of δ
Missing Data
Robust Estimations
Data Transformations
Biplot
Probabilistic PCA

Different Constraints on Different Dimensions (DCDD)
Model and Algorithm
Additional Constraints
Example 1
Example 2
Residual Analysis
Graphical Display of Oblique Components
Extended Redundancy Analysis (ERA)
Generalized Structured Component Analysis (GSCA)

Epilogue

Appendix

Bibliography

Index

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Wednesday, November 13, 2013

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