Computer Science Department
School of Computer Science, Carnegie Mellon University


Semin-Supervised Learning:
From Gaussian Fields to Gaussian Processes

Xiaojin Zhu, John Lafferty, Zoubin Ghahraman

August 2003

Keywords: Artificial intelligence, learning; pattern recognition, models-statistical; pattern recognition, design methodology-classifier design and evaluation; algorithms, semi-supervised learning, kernel, Gaussian processes

We show that the Gaussian random fields and harmonic energy minimizing function framework for semi-supervised learning can be viewed in terms of Gaussian processes, with covariance matrices derived from the graph Laplacian. We derive hyperparameter learning with evidence maximization, and give an empirical study of various ways to parameterize the graph weights.

21 pages

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