Advances in Principal Component Analysis: Research and Development

Description

This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA. Front Matter ....Pages i-vii Sparse Principal Component Analysis via Rotation and Truncation (Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu)....Pages 1-18 PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images (Aloke Datta, Susmita Ghosh, Ashish Ghosh)....Pages 19-46 Principal Component Analysis in the Presence of Missing Data (Marco Geraci, Alessio Farcomeni)....Pages 47-70 Robust PCAs and PCA Using Generalized Mean (Jiyong Oh, Nojun Kwak)....Pages 71-98 Principal Component Analysis Techniques for Visualization of Volumetric Data (Salaheddin Alakkari, John Dingliana)....Pages 99-120 Outlier-Resistant Data Processing with L1-Norm Principal Component Analysis (Panos P. Markopoulos, Sandipan Kundu, Shubham Chamadia, Nicholas Tsagkarakis, Dimitris A. Pados)....Pages 121-135 Damage and Fault Detection of Structures Using Principal Component Analysis and Hypothesis Testing (Francesc Pozo, Yolanda Vidal)....Pages 137-191 Principal Component Analysis for Exponential Family Data (Meng Lu, Kai He, Jianhua Z. Huang, Xiaoning Qian)....Pages 193-223 Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability (Yannick Deville, Charlotte Revel, Véronique Achard, Xavier Briottet)....Pages 225-252