A semi-supervised approach of graph-based with local and global consistency
by Yihao Zhang; Junhao Wen; Zhi Liu; Changpeng Zhu
International Journal of Information Technology and Management (IJITM), Vol. 18, No. 2/3, 2019

Abstract: An approach of graph-based semi-supervised learning is proposed that consider the local and global consistency of data. Like most graph-based semi-supervised learning, the algorithm mainly focused on two key issues: the graph construction and the manifold regularisation framework. In the graph construction, these labelled and unlabelled data are represented as vertices encoding edges weights with the similarity of instances, which means that not only the local geometry information but also the class information are utilised. In manifold regularisation framework, the cost function contains two terms of smoothness constraint and fitting constraint, it is sufficiently smooth with respect to the intrinsic structure revealed by known labelled and unlabelled instances. Specifically, we design the algorithm that uses the normalised Laplacian eigenvectors, which ensure the cost function can converge to closed form expression and then, we provide the convergence proof. Experimental results on various datasets and entity relationship classification show that the proposed algorithm mostly outperforms the popular classification algorithm.

Online publication date: Thu, 23-May-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information Technology and Management (IJITM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com