Enhanced Linear Discriminant Analysis Based on Canonical Correlation Analysis

To effectively mix the features which are extracted by Fisher linear discrininant analysis and maximum scatter difference discriminant analysis and form a feature set which can reflect the samples more comprehensive,an enhanced discriminant analysis method based on canonical correlation analysis is proposed in the paper.Fisher linear discriminant analysis(LDA) and Maximum Scatter Difference Discriminate Analysis(MSDDA) are firstly adopted to extract two sets of features in the same pattern space,respectively.The canonical correlation analysis method is then used to fuse the two sets of features obtained above and derives more effective canonical discriminant features.Finally,the extensive experiments are performed on ORL face database.The experimental results verify the effectiveness of the proposed method.