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When using canonical correlation analysis (CCA), we can integrate the dataset and label information via transforming the class label matrix Y into the class indicator matrix T. Such as: $T = (YY^T)^½Y$ in [this article on LS CCA][1].

While for the numeric dataset and the binary dataset, the binary dataset can be viewed as the class label matrix and transformed into the class indicator matrix. After that, should I use the CCA?

Though without this transformation, [the CCA can be used][2], I'd like to know how to explian this kind of transformation once using it on the binary dataset (not label matrix). Thank you.

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    $\begingroup$ This might be better suited for Cross Validated $\endgroup$ – Suresh Venkat May 23 '14 at 15:08
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    $\begingroup$ Well, I do, But nobody reply it. Consequently, I posted it here because I believe it's also a machine learning problem. $\endgroup$ – Zhilong Jia May 26 '14 at 1:26

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