A least squares formulation for canonical correlation analysis

@inproceedings{Sun2008ALS,
  title={A least squares formulation for canonical correlation analysis},
  author={Liang Sun and Shuiwang Ji and Jieping Ye},
  booktitle={ICML},
  year={2008}
}
Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables into a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction, in which one of the multi-dimensional variables is derived from the class label. It has been shown that CCA can be formulated as a least squares problem in the binaryclass case. However… CONTINUE READING
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