Title of article :
Generalized canonical correlation analysis for classification
Author/Authors :
Shen، نويسنده , , Cencheng and Sun، نويسنده , , Ming and Tang، نويسنده , , Minh and Priebe، نويسنده , , Carey E.، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 2014
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.
Keywords :
Generalized canonical correlation analysis (GCCA) , Low-dimensional projection , Stiefel manifold , Classification
Journal title :
Journal of Multivariate Analysis
Journal title :
Journal of Multivariate Analysis