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Discriminative Gaussian Process Latent Variable Models for Classification
Raquel Urtasun, trevor darrell

Citation
Raquel Urtasun, trevor darrell. "Discriminative Gaussian Process Latent Variable Models for Classification". ICML 2007, June, 2007.

Abstract
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.

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  • HTML
    Raquel Urtasun, trevor darrell. <a
    href="http://www.truststc.org/pubs/277.html"
    >Discriminative Gaussian Process Latent Variable Models
    for Classification</a>, ICML 2007, June, 2007.
  • Plain text
    Raquel Urtasun, trevor darrell. "Discriminative
    Gaussian Process Latent Variable Models for
    Classification". ICML 2007, June, 2007.
  • BibTeX
    @inproceedings{Urtasundarrell07_DiscriminativeGaussianProcessLatentVariableModelsFor,
        author = {Raquel Urtasun and trevor darrell},
        title = {Discriminative Gaussian Process Latent Variable
                  Models for Classification},
        booktitle = {ICML 2007},
        month = {June},
        year = {2007},
        abstract = {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.},
        URL = {http://www.truststc.org/pubs/277.html}
    }
    

Posted by trevor darrell on 30 Jul 2007.
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