Learning Canonical Correlations
نویسندگان
چکیده
This paper presents a novel learning algorithm that nds the linear combination of one set of multi-dimensional variates that is the best predictor, and at the same time nds the linear combination of another set which is the most predictable. This relation is known as the canonical correlation and has the property of being invariant with respect to a ne transformations of the two sets of variates. The algorithm successively nds all the canonical correlations beginning with the largest one.
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