  We combine K-means clustering with the least-squares kernel classification
method. K-means clustering is used to extract a set of representative vectors
for each class. The least-squares kernel method uses these representative
vectors as a training set for the classification task. We show that this
combination of unsupervised and supervised learning algorithms performs very
well, and we illustrate this approach using the MNIST dataset
