摘要：Problem statement: Kernel discriminative common vector (KDCV) was one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. Approach: This study presented a new algorithm called Boosting Kernel Discriminative Common Vector (BKDCV) to further improve the overall performance of KDCV by integrating the boosting and KDCV techniques. Results: In BKDCV, the feature selection and the classifier training were conducted by KDCV and AdaBoost.M2 respectively. To reduce the dependency between classifier outputs and to speed up the learning, each classifier was trained in the different feature space which was obtained by applying KDCV to a small set of hard-to-classify training samples. The proposed method BKDCV possessed several appealing properties. First, like all Kernel methods, it handled non-linearity in a disciplined manner. Second by introducing pair-wise class discriminant information into discriminant criterion, it further increased the classification accuracy. Third, by calculating significant discriminant information, within class scatter space, it also effectively contracted with the small sample size problem. Fourth, it constituted a strong ensemble based KDCV framework by taking advantage of boosting and KDCV techniques. Conclusion: This new method was applied on extended Yale B face database and achieves better classification accuracy. Experimental results demonstrated the promising performance of the proposed method as compared to the other methods.
关键词：Kernel discriminative common vectors; boosting; pair-wise class discriminant information; Adaboost.M2; small sample size problem; discriminant criterion
