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This book is indexed in
Computer and Information Science » Numerical Analysis and Scientific Computing
Principal Component Analysis
Edited by Parinya Sanguansat, ISBN 978-953-51-0195-6, Hard cover, 300 pages, Publisher: InTech, Chapters published March 02, 2012 under CC BY 3.0 license
DOI: 10.5772/2340 
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.
- Chapter 1
Two-Dimensional Principal Component Analysis and Its Extensions - Chapter 2 Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information
- Chapter 3 Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology
- Chapter 4 Subset Basis Approximation of Kernel Principal Component Analysis
- Chapter 5 Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor
- Chapter 6 Application of Linear and Nonlinear Dimensionality Reduction Methods
- Chapter 7 Acceleration of Convergence of the Alternating Least Squares Algorithm for Nonlinear Principal Components Analysis
- Chapter 8 The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance
- Chapter 9 FPGA Implementation for GHA-Based Texture Classification
- Chapter 10 The Basics of Linear Principal Components Analysis
- Chapter 11 Robust Density Comparison Using Eigenvalue Decomposition
- Chapter 12 Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis
- Chapter 13 On-Line Monitoring of Batch Process with Multiway PCA/ICA
- Chapter 14 Computing and Updating Principal Components of Discrete and Continuous Point Sets