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 language | English |
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Publication date | 2011 |
Publication status | Published - 2011 |
Event | Annual Conference on Neural Information Processing Systems : CMPL workshop - Granada, Spain Duration: 7 Mar 2012 → 8 Mar 2012 Conference number: 25 |
Workshop
Workshop | Annual Conference on Neural Information Processing Systems : CMPL workshop |
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Number | 25 |
Country/Territory | Spain |
City | Granada |
Period | 07/03/2012 → 08/03/2012 |