Boosting information fusion

Ensemble methods provide a principled framework for building high performance classifiers and representing many types of data. As a result, these methods can be useful for making inferences in many domains such as classification and multi-modal biometrics. We introduce a novel ensemble method for combining multiple representations (or views). The method is a multiple view generalization of AdaBoost. Similar to AdaBoost, base classifiers are independently built from each representation. Unlike AdaBoost, however, all data types share the same sampling distribution as the view whose weighted training error is the smallest among all the views. As a result, the most consistent data type dominates over time, thereby significantly reducing sensitivity to noise. In addition, our proposal is provably better than AdaBoost trained on any single type of data. The proposed method is applied to the problems of facial and gender prediction based on biometric traits as well as of protein classification. Experimental results show that our method outperforms several competing techniques including kernel-based data fusion.