Topic Modeling Ensembles
Shen, Zhiyong; Luo, Ping; Yang, Shengwen; Shen, Xukun
Keyword(s): Topic model, Ensemble
Abstract: In this paper we propose a framework of topic modeling ensembles, a novel solution to combine the models learned by topic modeling over each partition of the whole corpus. It has the potentials for applications such as distributed topic modeling for large corpora, and incremental topic modeling for rapidly growing corpora. Since only the base models, not the original documents, are required in the ensemble, all these applications can be performed in a privacy preserving manner. We explore the theoretical foundation of the proposed framework, give its geometric interpretation, and implement it for both PLSA and LDA. The evaluation of the implementations over the synthetic and real- life data sets shows that the proposed framework is much more efficient than modeling the original corpus directly while achieves comparable effectiveness in terms of perplexity and classification accuracy.
Additional Publication Information: To be published and presented at the Tenth IEEE International Conference on Data Mining, Sydney, Australia, December 14-17, 2010.
External Posting Date: October 21, 2010 [Fulltext]. Approved for External Publication
Internal Posting Date: October 21, 2010 [Fulltext]