A multi-round distributed CCA algorithm is shown to match the pooled-data convergence rate with vector-only communication and a gap-free error bound that avoids explicit eigenvalue-gap assumptions.
A probabilistic interpretation of canonical correlation analysis
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Distributed Estimation and Gap-Free Analysis of Canonical Correlations
A multi-round distributed CCA algorithm is shown to match the pooled-data convergence rate with vector-only communication and a gap-free error bound that avoids explicit eigenvalue-gap assumptions.