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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.

M. Belkin,P. Niyogi,V. Sindhwani
On  Manifold  Regularization, 2004