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Discriminative Gaussian Process Latent Variable Model for Classification

Author(s)
Urtasun, Raquel; Darrell, Trevor
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Advisor
Trevor Darrell
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Abstract
Supervised learning is difficult with high dimensional input spacesand very small training sets, but accurate classification may bepossible if the data lie on a low-dimensional manifold. GaussianProcess Latent Variable Models can discover low dimensional manifoldsgiven only a small number of examples, but learn a latent spacewithout regard for class labels. Existing methods for discriminativemanifold learning (e.g., LDA, GDA) do constrain the class distributionin the latent space, but are generally deterministic and may notgeneralize well with limited training data. We introduce a method forGaussian Process Classification using latent variable models trainedwith discriminative priors over the latent space, which can learn adiscriminative latent space from a small training set.
Date issued
2007-03-28
URI
http://hdl.handle.net/1721.1/36901
Other identifiers
MIT-CSAIL-TR-2007-021
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
Gaussian Processes, Classification, Latent Variable Models, Machine Learning

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