1. A training method for a classification system comprising: for each of a first set of training samples, providing a first view of the training sample and a second view of the training sample, each of the first and second views being defined in a respective original feature space; with a computer processor, learning an embedding function for embedding the first view of the training samples in the first set into a common feature space with the second view of the training samples, for each of a second set of labeled training samples, providing the first view of the labeled training labeled sample and a label selected from a finite set of labels; embedding the first view of each of the labeled training samples in the second set into the common feature space using the embedding function to generate embedded first views of the labeled training samples; training a classifier based on the embedded first view of each the labeled training samples in the second set which has been embedded in the common feature space and the respective label, without using the second view of the labeled training samples embedded in the common feature space.