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Discriminative Gaussian process latent variable model for classification

10 years 4 months ago
Discriminative Gaussian process latent variable model for classification
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional manifold. Gaussian Process Latent Variable Models can discover low dimensional manifolds given only a small number of examples, but learn a latent space without regard for class labels. Existing methods for discriminative manifold learning (e.g., LDA, GDA) do constrain the class distribution in the latent space, but are generally deterministic and may not generalize well with limited training data. We introduce a method for Gaussian Process Classification using latent variable models trained with discriminative priors over the latent space, which can learn a discriminative latent space from a small training set.
Raquel Urtasun, Trevor Darrell
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Raquel Urtasun, Trevor Darrell
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