| dc.contributor.advisor |
Trevor Darrell |
|
| dc.contributor.author |
Urtasun, Raquel |
|
| dc.contributor.author |
Darrell, Trevor |
|
| dc.contributor.other |
Vision |
|
| dc.date.accessioned |
2007-03-29T11:21:46Z |
|
| dc.date.available |
2007-03-29T11:21:46Z |
|
| dc.date.issued |
2007-03-28 |
|
| dc.identifier.other |
MIT-CSAIL-TR-2007-021 |
|
| dc.identifier.uri |
http://hdl.handle.net/1721.1/36901 |
|
| dc.description.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. |
|
| dc.description.provenance |
Made available in DSpace on 2007-03-29T11:21:46Z (GMT). No. of bitstreams: 2
MIT-CSAIL-TR-2007-021.ps: 1433667 bytes, checksum: d452e39bb918e0d0eaee260438e4bae3 (MD5)
MIT-CSAIL-TR-2007-021.pdf: 579634 bytes, checksum: c2cdd0227f239f15695e28a43e26571a (MD5)
Previous issue date: 2007-03-28 |
en |
| dc.format.extent |
8 p. |
|
| dc.relation.ispartofseries |
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
|
| dc.subject |
Gaussian Processes |
|
| dc.subject |
Classification |
|
| dc.subject |
Latent Variable Models |
|
| dc.subject |
Machine Learning |
|
| dc.title |
Discriminative Gaussian Process Latent Variable Model for Classification |
|
