Learning data manifolds with a Cutting Plane method
 Looks approximately like SVM: perform binary classification on a highdimensional manifold (or sets of manifolds in this case).
 The general idea behind Mcp_simple is to start with a finite number of training examples, find the maximum margin solution for that training set, augment the draining set by finiding a poing on the manifolds that violates the constraints, iterating the process until a tolerance criteria is met.
 The more complicated cutting plane SVM uses slack variables to allow solution where classification is not linearly separable.
 Propose using one slack variable per manifold, plus a manifold center, which strictly obeys the margin (classification) constraint.
 Much effort put to proving the convergence properties of these algorithms; admittedly I couldn't be bothered to read...
