  We consider the general problem of utilizing both labeled and unlabeled data
to improve data representation performance. A new semi-supervised learning
framework is proposed by combing manifold regularization and data
representation methods such as Non negative matrix factorization and sparse
coding. We adopt unsupervised data representation methods as the learning
machines because they do not depend on the labeled data, which can improve
machine's generation ability as much as possible. The proposed framework forms
the Laplacian regularizer through learning the affinity graph. We incorporate
the new Laplacian regularizer into the unsupervised data representation to
smooth the low dimensional representation of data and make use of label
information. Experimental results on several real benchmark datasets indicate
that our semi-supervised learning framework achieves encouraging results
compared with state-of-art methods.
