图 书 馆
独立因素主题模型Independent Factor Topic Models |
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| 课程网址: | http://videolectures.net/icml09_putthividhya_iftm/ |
| 主讲教师: | Duangmanee (Pew) Putthividhya |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text document modeling. In this paper, we improve upon CTM and propose Independent Factor Topic Models (IFTM) which use linear latent variable models to uncover the hidden sources of correlation between topics. There are 2 main contributions of this work. First, by using a sparse source prior model, we can directly visualize sparse patterns of topic correlations. Secondly, the conditional independence assumption implied in the use of latent source variables allows the objective function to factorize, leading to a fast Newton- Ralphson based variational inference algorithm. Experimental results on synthetic and real data show that IFTM runs on average 3-5 times faster than CTM, while giving competitive performance as measured by perplexity and log-likelihood of held-out data. |
| 关 键 词: | 主题模型; 独立因子; 可视化主题 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 22 |