1. A robust method for fusing data from different information sources, the method comprising: initially assigning weights to each of a plurality of training examples; and iteratively, for each view: separately training weak classifiers hk on a sample of training examples based on the distribution of the weights, selecting the weak classifier corresponding to the view with the lowest error rate, and updating the weights of the training examples based on the lowest error rate among all views.