On Sparse Multi-Task Gaussian Process Priors for Music Preference Learning

Jens Brehm Nielsen, Bjørn Sand Jensen, Jan Larsen

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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.
    Original languageEnglish
    Publication date2011
    Publication statusPublished - 2011
    EventAnnual Conference on Neural Information Processing Systems : CMPL workshop - Granada, Spain
    Duration: 7 Mar 20128 Mar 2012
    Conference number: 25

    Workshop

    WorkshopAnnual Conference on Neural Information Processing Systems : CMPL workshop
    Number25
    CountrySpain
    CityGranada
    Period07/03/201208/03/2012

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