Text Classification by Markov Random Walks with Reward.
Xueyuan Zhou, Chunping Li · 2005
Abstract – We propose a novel model for semisupervised classification by bringing in reward in Markov random walks. Both angle and distance metrics for vectors are combined in this model. Taking advantage of absorbing states, transient analysis of Markov chain can be performed more easily, based on Markov random walks. Diffusion of unlabeled data points makes our approach suffer less from error propagation for the classification process. The experiment results show that Markov random walks with reward can be efficiently applied in the semisupervised classification.