Matrix computations (3rd ed.)
A network for recursive extraction of canonical coordinates
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Probability and Random Processes for Electrical and Computer Engineers
Probability and Random Processes for Electrical and Computer Engineers
Canonical coordinates and the geometry of inference, rate, andcapacity
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
IEEE Transactions on Image Processing
Underwater target detection from multi-platform sonar imagery using multi-channel coherence analysis
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Hi-index | 0.00 |
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.