Partially labeled classification with Markov random walks

To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random walk representation exploits any low dimensional structure in the data in a robust, probabilistic manner. We develop and compare several estimation criteria/algorithms suited to this representation. This includes in particular multi-way classification with an average margin criterion which permits a closed form solution. The time scale of the random walk regularizes the representation and can be set through a margin-based criterion favoring unambiguous classification. We also extend this basic regularization by adapting time scales for individual examples. We demonstrate the approach on synthetic examples and on text classification problems.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Martin Szummer, Tommi Jaakkola
• Keywords: Markov Random Walk, NIPS 2001, NIPS 2007, Random Walk Representation, Unlabeled Examples