Semi-Supervised Learning Using Random Walk Limiting Probabilities

The semi-supervised learning paradigm allows that a large amount of unlabeled data be classified using just a few labeled data. To account for the minimal a priori label knowledge, the information provided by the unlabeled data is also used in the classification process. This paper describes a semi-supervised technique that uses random walk limiting probabilities to propagate label information. Each label is propagated through a network of unlabeled instances via a biased random walk. The probability of a vertex receiving a label is expressed in terms of the limiting conditions of the walk process. Simulations show that the proposed technique is competitive with benchmarked techniques.

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