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Download fileGraph-Based Semi-Supervised Learning as a Generative Model
journal contribution
posted on 2003-07-01, 00:00 authored by Jingrui He, Jaime G. Carbonell, Yan LiuThis 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.