Ganesh R Naik:

Ganesh R. Naik received B.E. degree in Electronics and Communication Engineering from the University of Mysore, India, in 1997. M.E. degree in Communication and Information Engineering from Griffith University, Brisbane, Australia, in 2002, and the PhD degree in the area of Electronics Engineering, specialised in Biomedical Engineering and Signal processing from RMIT University, Melbourne, Australia, in 2009.

He is currently Postdoctoral research fellow at MARCS institute, Western Sydney University. Prior to that he held Chancellor's Post-Doctoral Research Fellow position Centre for Health Technologies (CHT), University of Technology Sydney (UTS). As an early mid-career researcher, he has edited 10 books, authored more than 100 papers in peer reviewed journals, conferences, and book chapters over the last seven years. His research interests include EMG signal processing, Pattern recognition, Blind Source Separation (BSS) techniques, Biomedical signal processing, Human Computer Interface (HCI) and Audio signal processing. 

Theory.- Basic principles of PCA.- Geometric Principles of PCA.- Principal components and Correlation.- PCA in Regression analysis matrices.- PCA in cluster analysis.- PCA and factor analysis.- PCA for time series and independent data (ICA).- Sparse PCA.- Non-negative PCA.- Applications of PCA.- PCA for Electrocardiography (ECG) applications.- PCA for Electroencephalography (EEG) applications.- PCA for Electromyography (EMG) applications.- PCA for bioinformatics and gene expression applications.- PCA for human movement science applications.- PCA for Gait Kinematics for Patients with Knee Osteoarthritis.- Neuroscience and biomedical application of PCA.- PCA applications for Brain Computer Interface (BCI) and motor imagery tasks.- PCA for Image processing applications.- PCA for Video processing applications.- PCA for dimensional reduction applications.- PCA for financial and economics applications.

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.