1. A classifier training method comprising: providing a set of training samples each training sample comprising a training vector in a first multi-dimension space representative of an object and a class label for the object; for at least a subset of the training samples, deriving a set of embedding functions for embedding training vectors that are in the first multi-dimension space into a second multi-dimension space of higher dimensionality than the first multi-dimension space, the embedding functions comprising one embedding function for each dimension of the first multi-dimension space; with the embedding functions, generating a set of embedded vectors in the second multi-dimension space corresponding to the training vectors; with a computer processor, training a linear classifier on the set of embedded training vectors and their class labels to generate a trained linear classifier operative in the second multi-dimension space for predicting labels for unlabeled sample vectors.