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Semi-Supervised Classification Using Sparse Gaussian Process Regression

Patel, Amrish and Sundararajan, S and Shevade, Shirish (2010) Semi-Supervised Classification Using Sparse Gaussian Process Regression. In: 21st Internation Joint Conference on Artifical Intelligence (IJCAI-09), JUL 11-17, 2009 , Pasadena, CA, pp. 1193-1198.

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Official URL: http://portal.acm.org/citation.cfm?id=1661636

Abstract

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.

Item Type: Conference Paper
Additional Information: Copyright 2009 IEEE. Personal use of this material is permitted.However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 29 Nov 2010 11:24
Last Modified: 29 Nov 2010 11:24
URI: http://eprints.iisc.ernet.in/id/eprint/34072

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