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According to paper By Sun, Ji an Ye; A Least Squares Formulation for Canonical Correlation Analysis http://www.machinelearning.org/archive/icml2008/papers/270.pdf CCA can be reformulated as a least squares problem, where the L1 penalty can be added, and then it can be solved by LARS (least angle regression) which has a benefit for computing the whole solution path for all levels of regularization for a cost of fitting only one model.

I am bit lost in the math, so how is this possible? Is this true for any solver that can solve linear regression with l1 regularization? And most important, how can I reformulate my CCA problem, so it can be solved with LARS (and therefore for all regularization parameters at once)?

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