On sparse multi-task Gaussian process priors for music preference learning

Nielsen, J. B., Sand Jensen, B. and Larsen, J. (2011) On sparse multi-task Gaussian process priors for music preference learning. In: 25th Annual Conference on Neural Information Processing Systems : CMPL workshop, Granada, Spain, 12-17 Dec 2011, pp. 1-8.

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Abstract

In this paper we study pairwise preference learning in a music setting with multitask Gaussian processes and examine the effect of sparsity in the input space as well as in the actual judgments. To introduce sparsity in the inputs, we extend a classic pairwise likelihood model to support sparse, multi-task Gaussian process priors based on the pseudo-input formulation. Sparsity in the actual pairwise judgments is potentially obtained by a sequential experimental design approach, and we discuss the combination of the sequential approach with the pseudo-input preference model. A preliminary simulation shows the performance on a real-world music preference dataset which motivates and demonstrates the potential of the sparse Gaussian process formulation for pairwise likelihoods.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jensen, Dr Bjorn Sand
Authors: Nielsen, J. B., Sand Jensen, B., and Larsen, J.
College/School:College of Science and Engineering > School of Computing Science
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