图 书 馆
贝叶斯高斯过程潜变量模型Bayesian Gaussian process latent variable model |
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| 课程网址: | http://videolectures.net/aistats2010_titsias_bgp/ |
| 主讲教师: | Michalis K. Titsias |
| 开课单位: | 曼彻斯特大学 |
| 开课时间: | 2010-06-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method allows us to variationally integrate out the input variables of the Gaussian process and compute a lower bound on the exact marginal likelihood of the nonlinear latent variable model. The maximization of the variational lower bound provides a Bayesian training procedure that is robust to overfitting and can automatically select the dimensionality of the nonlinear latent space. We demonstrate our method on real world datasets. The focus in this paper is on dimensionality reduction problems, but the methodology is more general. For example, our algorithm is immediately applicable for training Gaussian process models in the presence of missing or uncertain inputs. |
| 关 键 词: | 变分推理; 贝叶斯非线性; 降维问题 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-22：王淑红(课程编辑志愿者) |
| 阅读次数: | 374 |