Canonical correlation analysis (CCA) algorithms for multiple data sets: Application to blind SIMO equalization

@article{Va2005CanonicalCA,
  title={Canonical correlation analysis (CCA) algorithms for multiple data sets: Application to blind SIMO equalization},
  author={Javier V{\'i}a and Ignacio Santamar{\'i}a and Jes{\'u}s P{\'e}rez},
  journal={2005 13th European Signal Processing Conference},
  year={2005},
  pages={1-4}
}
Canonical Correlation Analysis (CCA) is a classical tool in statistical analysis that measures the linear relationship between two or several data sets. In [1] it was shown that CCA of M = 2 data sets can be reformulated as a pair of coupled least squares (LS) problems. Here, we generalize this idea to M > 2 data sets. First, we present a batch algorithm to extract all the canonical vectors through an iterative regression procedure, which at each iteration uses as desired output the mean of the… CONTINUE READING
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