AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Semi-Supervised Classification using Sparse Gaussian Process Regression
Amrish Patel, S. Sundararajan, Shirish Shevade

Last modified: 2009-06-26


Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.


Semi-supervised Classification; Gaussian Processes; Sparse Models

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