Extensions of the informative vector machine

Authors:
Neil D. Lawrence;John C. Platt;Michael I. Jordan
Affiliations:
Department of Computer Science, University of Sheffield, Sheffield, U.K.;Microsoft Research, Microsoft Corporation, Redmond, WA;Computer Science and Statistics, University of California, Berkeley, CA
Venue:
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Year:
2004

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Abstract

The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by combining assumed density filtering with a heuristic for choosing points based on minimizing posterior entropy. This paper extends IVM in several ways. First, we propose a novel noise model that allows the IVM to be applied to a mixture of labeled and unlabeled data. Second, we use IVM on a block-diagonal covariance matrix, for “learning to learn” from related tasks. Third, we modify the IVM to incorporate prior knowledge from known invariances. All of these extensions are tested on artificial and real data.