图 书 馆
流上的文档集合的主题模型推断的有效方法Efficient Methods for Topic Model Inference on Streaming Document Collections |
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| 课程网址: | http://videolectures.net/kdd09_mimno_emft/ |
| 主讲教师: | David Mimno |
| 开课单位: | 麻省大学 |
| 开课时间: | 2009-12-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference techniques that are computationally expensive. With today's large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new documents without retraining the model. In this paper, we empirically evaluate the performance of several methods for topic inference in previously unseen documents, including methods based on Gibbs sampling, variational inference, and a new method inspired by text classification. The classification-based inference method produces results similar to iterative inference methods, but requires only a single matrix multiplication. In addition to these inference methods, we present SparseLDA, an algorithm and data structure for evaluating Gibbs sampling distributions. Empirical results indicate that SparseLDA can be approximately 20 times faster than traditional LDA and provide twice the speedup of previously published fast sampling methods, while also using substantially less memory. |
| 关 键 词: | 低维子空间的高维数据; 主题模型; 近似推理技术; 算法和数据评估 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：yumf |
| 阅读次数: | 127 |