PCA enhanced training data for adaboost
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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LPBoost seemingly should have better generalization capability than AdaBoost according to the margin theory [12] because LPBoost optimizes the minimum margin directly. Thus far, however, there is no empirical comparison and theoretical explanation of LPBoost against AdaBoost. We have conducted an experimental evaluation on the classification performance of LPBoost and AdaBoost in this paper. Our results show that the LPBoost performs worse than AdaBoost in most cases. By considering the margin distribution, we present an explanation. Also, our finding indicates that besides the minimum margin, which is directly and globally optimized in LPBoost, the margin distribution plays a more important role in terms of the learned strong classifier’s classification performance.