图 书 馆
拓扑约束潜变量模型Topologically-Constrained Latent Variable Models |
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| 课程网址: | http://videolectures.net/icml08_urtasun_topcon/ |
| 主讲教师: | Raquel Urtasun |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2008-07-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion data we expect latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In this paper, we present a range of approaches for embedding data in a non-Euclidean latent space. Our focus is the Gaussian Process latent variable model. In the context of human motion modeling this allows us to (a) learn models with interpretable latent directions enabling, for example, style/content separation, and (b) generalize beyond the data set enabling us to learn transitions between motion styles even though such transitions are not present in the data |
| 关 键 词: | 降维方法; 欧几里得空间; 潜变量模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 67 |