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What is Alternating Regression and how is it used for Canonical Correlation Analysis?
How does Alternating Regression work in CCA?
Alternating regression in CCA involves iteratively estimating the canonical correlation between two sets of variables while regressing one set of variables on the other. The algorithm starts by computing the correlation matrix between the two variable sets and then selecting the pair of variables that have the highest correlation as the first set of canonical variables. The algorithm then regresses the remaining variables from one set onto the canonical variables from the other set, and similarly for the other set of variables. This process is repeated until convergence is achieved.