"Principal Component Analysis" ed. by Parinya Sanguansat

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.

Contents
Preface
1. Two-Dimensional Principal Component Analysis and Its Extensions
2. Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information
3. Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology
4. Subset Basis Approximation of Kernel Principal Component Analysis
5. Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor
6. Application of Linear and Nonlinear Dimensionality Reduction Methods
7. Acceleration of Convergence of the Alternating Least Squares Algorithm for Nonlinear Principal Components Analysis
8. The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance
9. FPGA Implementation for GHA-Based Texture Classification
10. The Basics of Linear Principal Components Analysis
11. Robust Density Comparison Using Eigenvalue Decomposition
12. Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis
13. On-Line Monitoring of Batch Process with Multiway PCA/ICA
14. Computing and Updating Principal Components

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