PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Mr. LDA: a flexible large scale topic modeling package using variational inference in MapReduce
Proceedings of the 21st international conference on World Wide Web
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Statistical topic models such as the Latent Dirichlet Al- location (LDA) have emerged as an attractive framework to model, visualize and summarize large document collec- tions in a completely unsupervised fashion. Considering the enormous sizes of the modern electronic document col- lections, it is very important that these models are fast and scalable. In this work, we build parallel implementations of the variational EM algorithm for LDA in a multiproces- sor architecture as well as a distributed setting. Our ex- periments on various sized document collections indicate that while both the implementations achieve speed-ups, the distributed version achieves dramatic improvements in both speed and scalability. We also analyze the costs associated with various stages of the EM algorithm and suggest ways to further improve the performance.