Sparse Topic Models
Sparse Topic Models is a type of probabilistic topic model that constrains the distribution of topics over words or documents to be sparse, thereby enhancing interpretability by emphasizing a few dominant topics or words while suppressing negligible associations.
147
Publications
10.3K
Citations
419
Authors
177
Institutions
Publications per year
2017–2026
62
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A biterm topic model for short texts
Xiaohui Yan, Jiafeng Guo, Yanyan Lan et al. · 2013 · 1.1K citations
Wei Li, Andrew McCallum · 2006 · 619 citations
BTM: Topic Modeling over Short Texts
Xueqi Cheng, Xiaohui Yan, Yanyan Lan et al. · IEEE Transactions on Knowledge and Data Engineering · 2014 · 555 citations
On Smoothing and Inference for Topic Models
Arthur Asuncion, Max Welling, Padhraic Smyth et al. · arXiv (Cornell University) · 2012 · 452 citations · Full text
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Moscow Institute of Physics and Technology, Russia
5 publications · 232 citations
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177
Carnegie Mellon University
Pittsburgh, United States
10 h-index
25 publications
2.3K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
33 publications
7.5K citations
Wuhan University
Wuhan, China
7 h-index
24 publications
2.4K citations

Moscow Institute of Physics and Technology
Dolgoprudnyy, Russia
5 h-index
7 publications
281 citations
Institute of Computing Technology
Beijing, China
5 h-index
22 publications
7.6K citations
Princeton University
Princeton, United States
5 h-index
9 publications
1.2K citations

University of Massachusetts Amherst
Amherst Center, United States
5 h-index
10 publications
1.8K citations
Columbia University
New York, United States
4 h-index
4 publications
219 citations
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Repository · 11 publications · 907 citations
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Conference · 5 publications · 221 citations
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TKDE · 5 publications · 951 citations
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4 publications · 239 citations
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TPAMI · 2 publications · 48 citations
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