An Iterative Learning Algorithm for Multi-Channel Coherence Analysis

Author

Thompson, Bryan D. ; Azimi-Sadjadi, Mahmood R.

Author_Institution

Colorado State Univ., Fort Collins

fYear

2007

fDate

12-17 Aug. 2007

Firstpage

1326

Lastpage

1331

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Conference_Location

Orlando, FL

ISSN

1098-7576

Print_ISBN

978-1-4244-1379-9

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2007.4371150

Filename

4371150