图 书 馆
贝塔过程先验的非参数因子分析Nonparametric Factor Analysis with Beta Process Priors |
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| 课程网址: | http://videolectures.net/icml09_paisley_nfa/ |
| 主讲教师: | John Paisley |
| 开课单位: | 杜克大学 |
| 开课时间: | 2009-09-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BPFA) model allows for a dataset to be decomposed into a linear combination of a sparse set of factors, providing information on the underlying structure of the observations. As with the Dirichlet process, the beta process is a fully Bayesian conjugate prior, which allows for analytical posterior calculation and straightforward inference. We derive a variational Bayes inference algorithm and demonstrate the model on the MNIST digits and HGDP-CEPH cell line panel datasets. |
| 关 键 词: | 因子分析; 非参数扩展; 线性组合 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 245 |