图 书 馆
皮特曼-尤尔用幂律主题模型Topic Models with Power-Law Using Pitman-Yor Process |
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| 课程网址: | http://videolectures.net/kdd2010_sato_tmplupyp/ |
| 主讲教师: | Issei Sato |
| 开课单位: | 东京大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | One important approach for knowledge discovery and data mining is to estimate unobserved variables because latent variables can indicate hidden specific properties of observed data. The latent factor model assumes that each item in a record has a latent factor; the co-occurrence of items can then be modeled by latent factors. In document modeling, a record indicates a document represented as a ``bag of words,'' meaning that the order of words is ignored, an item indicates a word and a latent factor indicates a topic. Latent Dirichlet allocation (LDA) is a widely used Bayesian topic model applying the Dirichlet distribution over the latent topic distribution of a document having multiple topics. LDA assumes that latent topics, i.e., discrete latent variables, are distributed according to a multinomial distribution whose parameters are generated from the Dirichlet distribution. LDA also models a word distribution by using a multinomial distribution whose parameters follows the Dirichlet distribution. This Dirichlet-multinomial setting, however, cannot capture the power-law phenomenon of a word distribution, which is known as Zipf’s law in linguistics. We therefore propose a novel topic model using the Pitman-Yor(PY) process, called the PY topic model. The PY topic model captures two properties of a document; a power-law word distribution and the presence of multiple topics. In an experiment using real data, this model outperformed LDA in document modeling in terms of perplexity. |
| 关 键 词: | 狄利克雷分配; 贝叶斯; 幂律现象 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-01：汪洁炜(课程编辑志愿者) |
| 阅读次数: | 375 |