Fusion of covariance matrices of PCA and FLD

In this paper, we propose a novel approach for fusing two classifiers, specifically classifiers based on subspace analysis, during feature extraction. A method of combining the covariance matrices of the Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) is presented. Unlike other existing fusion strategies which fuse classifiers either at data level, or at feature level or at decision level, the proposed work combines two classifiers while extracting features introducing a new unexplored area for further research. The covariance matrices of PCA and FLD are combined using a product rule to preserve the natures of both covariance matrices with an expectation to have an increased performance. In order to show the effectiveness of the proposed fusion method, we have conducted a visual simulation on iris data. The proposed model has also been tested by performing clustering on standard datasets such as Zoo, Wine, and Iris. To study the versatility of the proposed method we have carried out an experimentation on sports video shot retrieval problem. The experimental results signify that the proposed fusing approach has an improved performance over individual classifiers.