| Abstract: |
Graph-based semi-supervised learning has been very active in recent years.
In this talk, we propose 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 using both the labeled and the unlabeled data.
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. |