Manifold  Regularization is a family of learning algorithms based  on  a  new  form of  regularization with which both labeled and unlabeled data can be incorporated in a general purpose learner.  Transductive Graph Learning algorithms and  standard methods like SVM and Regularized Least Squares can be obtained as special cases of this framework.