Graph-Based Semi-Supervised Learning as a Generative Model
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2492-2496.
Abstract
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.
@inproceedings{ijcai2007p401,
title = {Graph-Based Semi-Supervised Learning as a Generative Model},
author = {He, Jingrui and Carbonell, Jaime G. and Liu, Yan},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2492--2496},
year = {2007},
url = {https://ijcai.org/proceedings/2007/401},
}
TY - CPAPER TI - Graph-Based Semi-Supervised Learning as a Generative Model AU - He, Jingrui AU - Carbonell, Jaime G. AU - Liu, Yan T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2492 EP - 2496 UR - https://ijcai.org/proceedings/2007/401 ER -Copied
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