Advances in Principal Component Analysis

Chapter 1 Sparse Principal Component Analysis via Rotation and Truncation

Chapter 2 PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images

Chapter 3 Principal Component Analysis in the Presence of Missing Data

Chapter 4 Robust PCAs and PCA Using Generalized Mean

Chapter 5 Principal Component Analysis Techniques for Visualization of Volumetric Data

Chapter 6 Outlier-Resistant Data Processing with L1-Norm Principal Component Analysis

Chapter 7 Damage and Fault Detection of Structures Using Principal Component Analysis and Hypothesis Testing

Chapter 9 Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability