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Generalized Canonical Correlation Analysis for Classification

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 نشر من قبل Cencheng Shen
 تاريخ النشر 2013
  مجال البحث الاحصاء الرياضي
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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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