图 书 馆
贝叶斯高斯过程潜在变量模型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: |  我们引入了一个变分推理框架来训练高斯过程潜在变量模型，从而进行贝叶斯非线性降维。这种方法使我们能够对高斯过程的输入变量进行变分积分，并为非线性潜在变量模型的精确边际可能性计算一个下限。变分下界的最大化提供了一种贝叶斯训练程序，该程序对于过度拟合具有鲁棒性，并且可以自动选择非线性潜在空间的维数。我们在现实世界的数据集上演示我们的方法。本文的重点是降维问题，但该方法更为通用。例如，在缺少输入或不确定输入的情况下，我们的算法可立即应用于训练高斯过程模型。 |
| 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: | 变分积分; 变量模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-08：zyk |
| 最后编审: | 2020-12-16：zyk |
| 阅读次数: | 125 |