Generalized Canonical Correlation Analysis for Classification

Published 30 Apr 2013 in stat.ML | (1304.7981v5)

Abstract: For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulations and a real data experiment.

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