Title :
An Iterative Learning Algorithm for Multi-Channel Coherence Analysis
Author :
Thompson, Bryan D. ; Azimi-Sadjadi, Mahmood R.
Author_Institution :
Colorado State Univ., Fort Collins
Abstract :
An iterative learning algorithm for multi-channel coherence analysis (MCA) is developed in this paper. MCA is an extension of the well known canonical correlation analysis (CCA) that allows for more than two data channels to be analyzed. The many applications of CCA have motivated this extension to exploit the linear relationship between many data channels. This paper discusses fundamental differences between the two analysis techniques while reviewing the standard method for performing MCA. Discussion on why MCA correlations are not deemed "canonical" as they are in the two-channel case of CCA is also provided. The developed iterative learning for MCA is then demonstrated and its performance evaluated on a synthesized data set.
Keywords :
coherence; correlation methods; iterative methods; learning systems; canonical correlation analysis; data channels; iterative learning algorithm; multichannel coherence analysis; Algorithm design and analysis; Data mining; Image analysis; Iterative algorithms; Iterative methods; Mutual information; Neural networks; Radar signal processing; Signal analysis; Signal processing algorithms;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371150
