Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations-Reference-Cited by-同舟云学术

Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations

Published:2022-12-01 Issue:6 Volume:50 Page:
ISSN:0090-5364
Container-title:The Annals of Statistics
language:
Short-container-title:Ann. Statist.

Author:

Bing Xin¹,Bunea Florentina²,Strimas-Mackey Seth²,Wegkamp Marten³

Affiliation:

1. Department of Statistical Sciences, University of Toronto

2. Department of Statistics and Data Science, Cornell University

3. Departments of Mathematics, and of Statistics and Data Science, Cornell University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference38 articles.

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3. ARORA, S., GE, R., KOEHLER, F., MA, T. and MOITRA, A. (2016). Provable algorithms for inference in topic models. In Proceedings of the 33rd International Conference on Machine Learning (M. F. Balcan and K. Q. Weinberger, eds.). Proceedings of Machine Learning Research 48 2859–2867. PMLR, New York, New York, USA.

4. ARORA, S., GE, R. and MOITRA, A. (2012). Learning topic models—going beyond SVD. In 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science—FOCS 2012 1–10. IEEE Computer Soc., Los Alamitos, CA.

5. BANSAL, T., BHATTACHARYYA, C. and KANNAN, R. (2014). A provable SVD-based algorithm for learning topics in dominant admixture corpus. Adv. Neural Inf. Process. Syst. 27.

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1. Assigning topics to documents by successive projections;The Annals of Statistics;2023-10-01

2. Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations;The Annals of Statistics;2022-12-01