Boosting Bayesian MAP Classification

In this paper we redefine and generalize the classic k-nearest neighbors (k-NN) voting rule in a Bayesian maximum-a-posteriori (MAP) framework. Therefore, annotated examples are used for estimating pointwise class probabilities in the feature space, thus giving rise to a new instance-based classification rule. Namely, we propose to "boost" the classic k-NN rule by inducing a strong classifier from a combination of sparse training data, called "prototypes". In order to learn these prototypes, our MapBoost algorithm globally minimizes a multiclass exponential risk defined over the training data, which depends on the class probabilities estimated at sample points themselves. We tested our method for image categorization on three benchmark databases. Experimental results show that MapBoost significantly outperforms classic k-NN (up to 8%). Interestingly, due to the supervised selection of sparse prototypes and the multiclass classification framework, the accuracy improvement is obtained with a considerable computational cost reduction.