Semi-supervised Data Representation via Affinity Graph Learning

TL;DR

This paper introduces a semi-supervised learning framework that combines manifold regularization with data representation techniques like NMF and sparse coding, leveraging affinity graph learning to improve data representations using both labeled and unlabeled data.

Contribution

It proposes a novel semi-supervised framework integrating affinity graph learning with data representation methods, enhancing data representation by utilizing both labeled and unlabeled data.

Findings

Achieves improved data representations compared to state-of-the-art methods.

Effectively incorporates label information into unsupervised data representations.

Demonstrates strong performance on benchmark datasets.

Abstract

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.