Graph-based semi-supervised learning as a generative model

Jingrui He, Jaime Carbonell, Yan Liu

Research output: Contribution to journalConference articlepeer-review


This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show that the proposed method is superior to existing graph-based semi-supervised learning methods, especially when the labeled subset alone proves insufficient to estimate meaningful class priors.

Original languageEnglish (US)
Pages (from-to)2492-2497
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2007
Externally publishedYes
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Graph-based semi-supervised learning as a generative model'. Together they form a unique fingerprint.

Cite this