图 书 馆
一种潜在dirichlet分配谱算法A Spectral Algorithm for Latent Dirichlet Allocation |
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| 课程网址: | http://videolectures.net/machine_hsu_algorithm/ |
| 主讲教师: | Daniel Hsu |
| 开课单位: | 微软公司 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Topic modeling is a generalization of clustering that posits that observations (words in a document) are generated by \emph{multiple} latent factors (topics), as opposed to just one. This increased representational power comes at the cost of a more challenging unsupervised learning problem of estimating the topic-word distributions when only words are observed, and the topics are hidden. This work provides a simple and efficient learning procedure that is guaranteed to recover the parameters for a wide class of topic models, including Latent Dirichlet Allocation (LDA). For LDA, the procedure correctly recovers both the topic-word distributions and the parameters of the Dirichlet prior over the topic mixtures, using only trigram statistics (\emph{i.e.}, third order moments, which may be estimated with documents containing just three words). The method, called Excess Correlation Analysis, is based on a spectral decomposition of low-order moments via two singular value decompositions (SVDs). Moreover, the algorithm is scalable, since the SVDs are carried out only on k×k matrices, where k is the number of latent factors (topics) and is typically much smaller than the dimension of the observation (word) space. |
| 关 键 词: | 主题建模; 聚类; 表征能力 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-04-26：chenxin |
| 阅读次数: | 48 |