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2008

A Unifying Discussion of Correlation Analysis for Complex Random Vectors

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A Unifying Discussion of Correlation Analysis for Complex Random Vectors
The assessment of multivariate association between two complex random vectors is considered. A number of correlation coefficients based on three popular correlation analysis techniques, namely canonical correlation analysis, multivariate linear regression, and partial least squares, are reviewed and connected to performance measures in signal processing and communications, such as mean-squared estimation error, mutual information, and signal-to-noise ratio (SNR). For complex data, there are three types of correlation coefficients, which account for rotational, reflectional, and total (i.e., rotational and reflectional) dependencies between two random vectors. These three types are defined and analyzed for different correlation coefficients, and a numerical example is given. It is often required to compare two complex random vectors in a lower-dimensional subspace. For the large class of increasing, Schur-convex correlation coefficients, it is shown that the low-rank approximations of t...
Peter J. Schreier
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TSP
Authors Peter J. Schreier
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