CLASSIFICATION WITH BOOSTED DYADIC KERNEL DISCRIMINANTS

A method learns a binary classifier for classifying samples into a first class and a second class. First, a set of training samples is acquired. Each training sample is labeled as either belonging to the first class or to the second class. Pairs of dyadic samples are connected by projection vectors such that a first sample of each dyadic pair belonging to the first class and a second sample of each dyadic pair belonging to the second class. A set of hyperplanes are formed so that the hyperplanes have a surface normal to the projection vectors. One hyperplane from the set of hyperplanes is selected that minimizes a weighted classification error. The set of training samples is then weighted according to a classification by the selected hyperplane. The selected hyperplanes are combined into a binary classifier, and the selecting, weighting, and combining are repeated a predetermined number of iterations to obtain a final classifier for classifying test samples into the first and second classes.