Reading Group: Gaussian Processes for Ordinal Regression

SpeakerAndre Marquand
AffiliationDepartment of Neuroimaging, KCL
DateWednesday, 01 Jun 2011
Time14:00 - 15:00
LocationFoster Court 235
Event seriesMachine Learning for Neuroimaging Reading Group

We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation prop- agation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support vector machines on some benchmark and real-world data sets, including applications of ordinal regression to collaborative filtering and gene expression analysis. Experimental results on these data sets verify the usefulness of our approach.

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