# What's the meaning of the class indicator matrix when transforming the class label matrix into it in canonical correlation analysis?

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.

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