Iterative multi-channel coherence analysis with applications.
An iterative learning algorithm for performing Multi-Channel Coherence Analysis (MCCA) is developed in this paper. MCCA is an extension of the well-known Canonical Correlation Analysis (CCA) that allows for more than two data channels to be analyzed. This paper discusses a standard method for performing MCCA and compares it to a newly developed data-driven and iterative approach. The proposed algorithm is then tested on two examples and its performance is evaluated in terms of estimation errors with respect to the values obtained using the standard MCCA algorithm. The first example uses a synthesized data set while the second example uses a real data set based on multi-spectral satellite imagery of the Earth's surface.
Thompson
Bryan D
BD
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA. BryanDavidThompson@gmail.com
Azimi-Sadjadi
Mahmood R
MR
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D017215:Satellite Communications; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique