Advances in Principal Component Analysis

1. Sparse Principal Component Analysis via Rotation and Truncation
Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

2. PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images
Aloke Datta, Susmita Ghosh, Ashish Ghosh

3. Principal Component Analysis in the Presence of Missing Data
Marco Geraci, Alessio Farcomeni

4. Robust PCAs and PCA Using Generalized Mean
Jiyong Oh, Nojun Kwak

5. Principal Component Analysis Techniques for Visualization of Volumetric Data
Salaheddin Alakkari, John Dingliana

6. Outlier-Resistant Data Processing with L1-Norm Principal Component Analysis
Panos P. Markopoulos, Sandipan Kundu, Shubham Chamadia, Nicholas Tsagkarakis, Dimitris A. Pados

7. Damage and Fault Detection of Structures Using Principal Component Analysis and Hypothesis Testing
Francesc Pozo, Yolanda Vidal

8. Principal Component Analysis for Exponential Family Data
Meng Lu, Kai He, Jianhua Z. Huang, Xiaoning Qian

9. Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability
Yannick Deville, Charlotte Revel, Véronique Achard, Xavier Briottet

Avainsanat: Engineering, Signal, Image and Speech Processing, Pattern Recognition, Computational Intelligence, Computational Mathematics and Numerical Analysis, Biomedical Engineering