Semi-supervised Data Representation via Affinity Graph Learning
This work addresses data representation challenges in semi-supervised learning, but it appears incremental as it builds on existing methods without a major breakthrough.
The paper tackles the problem of improving data representation by combining labeled and unlabeled data, proposing a semi-supervised framework that integrates manifold regularization with methods like non-negative matrix factorization and sparse coding, and achieves encouraging results on benchmark datasets compared to state-of-the-art methods.
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.