ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform
Recently, topic models such as Latent Dirichlet Allocation (LDA) have been widely used in large-scale web mining. Many large-scale LDA training systems have been developed, which usually prefer a customized design from top to bottom with sophisticated synchronization support. We propose an LDA train...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2018-03-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020006 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568926551146496 |
|---|---|
| author | Bo Zhao Hucheng Zhou Guoqiang Li Yihua Huang |
| author_facet | Bo Zhao Hucheng Zhou Guoqiang Li Yihua Huang |
| author_sort | Bo Zhao |
| collection | DOAJ |
| description | Recently, topic models such as Latent Dirichlet Allocation (LDA) have been widely used in large-scale web mining. Many large-scale LDA training systems have been developed, which usually prefer a customized design from top to bottom with sophisticated synchronization support. We propose an LDA training system named ZenLDA, which follows a generalized design for the distributed data-parallel platform. The novelty of ZenLDA consists of three main aspects: (1) it converts the commonly used serial Collapsed Gibbs Sampling (CGS) inference algorithm to a Monte-Carlo Collapsed Bayesian (MCCB) estimation method, which is embarrassingly parallel; (2) it decomposes the LDA inference formula into parts that can be sampled more efficiently to reduce computation complexity; (3) it proposes a distributed LDA training framework, which represents the corpus as a directed graph with the parameters annotated as corresponding vertices and implements ZenLDA and other well-known inference methods based on Spark. Experimental results indicate that MCCB converges with accuracy similar to that of CGS, while running much faster. On top of MCCB, the ZenLDA formula decomposition achieved the fastest speed among other well-known inference methods. ZenLDA also showed good scalability when dealing with large-scale topic models on the data-parallel platform. Overall, ZenLDA could achieve comparable and even better computing performance with state-of-the-art dedicated systems. |
| format | Article |
| id | doaj-art-964e322084df4408ae89183ef6bfabdd |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-964e322084df4408ae89183ef6bfabdd2025-02-02T23:47:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-03-0111577410.26599/BDMA.2018.9020006ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel PlatformBo Zhao0Hucheng Zhou1Guoqiang Li2Yihua Huang3<institution content-type="dept">National Key Laboratory for Novel Software Technology</institution>, <institution>Nanjing University</institution>, <city>Nanjing</city> <postal-code>210023</postal-code>, <country>China</country> and <institution>Collaborative Innovation Center of Novel Software Technology and Industrialization</institution>, <city>Nanjing </city><postal-code>210023</postal-code>, <country>China</country>.<institution>Microsoft Research</institution>, <city>Beijing</city> <postal-code>100080</postal-code>, <country>China</country>.<institution>Huawei Technologies Co., Ltd.</institution>, <city>Shenzhen</city> <postal-code>518129</postal-code>, <country>China</country>.<institution content-type="dept">National Key Laboratory for Novel Software Technology</institution>, <institution>Nanjing University</institution>, <city>Nanjing</city> <postal-code>210023</postal-code>, <country>China</country> and <institution>Collaborative Innovation Center of Novel Software Technology and Industrialization</institution>, <city>Nanjing </city><postal-code>210023</postal-code>, <country>China</country>.Recently, topic models such as Latent Dirichlet Allocation (LDA) have been widely used in large-scale web mining. Many large-scale LDA training systems have been developed, which usually prefer a customized design from top to bottom with sophisticated synchronization support. We propose an LDA training system named ZenLDA, which follows a generalized design for the distributed data-parallel platform. The novelty of ZenLDA consists of three main aspects: (1) it converts the commonly used serial Collapsed Gibbs Sampling (CGS) inference algorithm to a Monte-Carlo Collapsed Bayesian (MCCB) estimation method, which is embarrassingly parallel; (2) it decomposes the LDA inference formula into parts that can be sampled more efficiently to reduce computation complexity; (3) it proposes a distributed LDA training framework, which represents the corpus as a directed graph with the parameters annotated as corresponding vertices and implements ZenLDA and other well-known inference methods based on Spark. Experimental results indicate that MCCB converges with accuracy similar to that of CGS, while running much faster. On top of MCCB, the ZenLDA formula decomposition achieved the fastest speed among other well-known inference methods. ZenLDA also showed good scalability when dealing with large-scale topic models on the data-parallel platform. Overall, ZenLDA could achieve comparable and even better computing performance with state-of-the-art dedicated systems.https://www.sciopen.com/article/10.26599/BDMA.2018.9020006latent dirichlet allocationcollapsed gibbs samplingmonte-carlograph computinglarge-scale machine learning |
| spellingShingle | Bo Zhao Hucheng Zhou Guoqiang Li Yihua Huang ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform Big Data Mining and Analytics latent dirichlet allocation collapsed gibbs sampling monte-carlo graph computing large-scale machine learning |
| title | ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform |
| title_full | ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform |
| title_fullStr | ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform |
| title_full_unstemmed | ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform |
| title_short | ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform |
| title_sort | zenlda large scale topic model training on distributed data parallel platform |
| topic | latent dirichlet allocation collapsed gibbs sampling monte-carlo graph computing large-scale machine learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020006 |
| work_keys_str_mv | AT bozhao zenldalargescaletopicmodeltrainingondistributeddataparallelplatform AT huchengzhou zenldalargescaletopicmodeltrainingondistributeddataparallelplatform AT guoqiangli zenldalargescaletopicmodeltrainingondistributeddataparallelplatform AT yihuahuang zenldalargescaletopicmodeltrainingondistributeddataparallelplatform |