Generalized canonical correlation analysis for classification. Shen, C., Sun, M., Tang, M., & Priebe, C., E. Journal of Multivariate Analysis, 130:310-322, 9, 2014.
abstract   bibtex   
For multiple multivariate datasets, we derive conditions under which Generalized Canonical Correlation Analysis improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis using only two data sets. We illustrate our theoretical results with simulations and a real data experiment.
@article{
 title = {Generalized canonical correlation analysis for classification},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {62H20,62H30,Classification,Generalized canonical correlation analysis (GCCA),Low-dimensional projection,Stiefel manifold},
 pages = {310-322},
 volume = {130},
 websites = {http://www.sciencedirect.com/science/article/pii/S0047259X14001201},
 month = {9},
 id = {eeadfe8b-ad72-3ed8-9ba6-057bee7b1f5f},
 created = {2015-06-19T08:32:46.000Z},
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 abstract = {For multiple multivariate datasets, we derive conditions under which Generalized Canonical Correlation Analysis improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis using only two data sets. We illustrate our theoretical results with simulations and a real data experiment.},
 bibtype = {article},
 author = {Shen, Cencheng and Sun, Ming and Tang, Minh and Priebe, Carey E.},
 journal = {Journal of Multivariate Analysis}
}

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