used to perform Feature Extraction and data interpretation, esp. for two relevant random vectors
Find optimal linear projections for two relevant random vectors to maximize their correlation in the new low dimensional space.
For 2 domains (2 domains of variables collected on the same samples), find (low dimensional) projections using (one or more) canonical variates (basis) such that the two projections are (maximum) correlated and in the same space.
identify and measure the correlation among two sets of random variables (random vectors), which leads to a new (common) representation capturing the maximum correlation between two random vectors
Data Centralization must be done prior.
Correlation → plays a role of normalization to make the correlation invariant with respect to rescaling a and b (canonical variates)
Math
CCA math
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Algorithm
Proportion of Variance
PoV(k) works up to min(p, q) can be used to find optimum intrinsic dimensionalityProportion of Variance
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