Advances in Principal Component Analysis Research and Development /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Naik, Ganesh R. (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Subjects: Signal processing.; Image processing.; Speech processing systems.; Pattern recognition.; Computational intelligence.; Computer mathematics.; Biomedical engineering.; Signal, Image and Speech Processing.; Pattern Recognition.; Computational Intelligence.; Computational Mathematics and Numerical Analysis.; Biomedical Engineering and Bioengineering.

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 man...

Table of Contents:

• 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.