Canonical Correlation Analysis: Algorithms And Their Application To Pattern Classification

Canonical Correlation Analysis (CCA) is a important research subject of multivariate statistical analysis. With the idea of principal component analysis, CCA reflects the linear correlation between two sets of variables with a few basis vectors. Now, the method has been applied to many fields for correlation analysis and forecast analysis, But there is few relevant reference about applying it to the pattern classification.Firstly, the linear algorithms and the nonlinear algorithms of CCA are presented in this paper. Secondly, some experiments have been designed to summarize and compare the characteristics of various kinds of algorithms. Their feasibility and validity in pattern recognition is confirmed by the experiments and theory. Finally, to apply the method in multiple classifiers, the combination of multiple classifiers based on belief value is performed . Experiments based on the ISAR radar airplane data demonstrate an improvement of the proposed method on classification performance .