• Corpus ID: 15107331

# Generalized Topic Modeling

@article{Blum2016GeneralizedTM,
title={Generalized Topic Modeling},
author={Avrim Blum and Nika Haghtalab},
journal={ArXiv},
year={2016},
volume={abs/1611.01259}
}
• Published 4 November 2016
• Computer Science
• ArXiv
Recently there has been significant activity in developing algorithms with provable guarantees for topic modeling. In standard topic models, a topic (such as sports, business, or politics) is viewed as a probability distribution $\vec a_i$ over words, and a document is generated by first selecting a mixture $\vec w$ over topics, and then generating words i.i.d. from the associated mixture $A{\vec w}$. Given a large collection of such documents, the goal is to recover the topic vectors and then…
1 Citations

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