Discriminative Gaussian process latent variable model for classification

@inproceedings{Urtasun2007DiscriminativeGP,
  title={Discriminative Gaussian process latent variable model for classification},
  author={Raquel Urtasun and Trevor Darrell},
  booktitle={ICML},
  year={2007}
}
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… CONTINUE READING
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Gaussian process models for visualisation of high dimensional data

  • N. D. Lawrence
  • Advances in Neural Information Processing Systems…
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