Powered Dirichlet Process - Controlling the “Rich-Get-Richer” Assumption in Bayesian Clustering-Reference-Cited by-同舟云学术

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Powered Dirichlet Process - Controlling the “Rich-Get-Richer” Assumption in Bayesian Clustering

Published:2023 Issue: Volume: Page:611-626
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases: Research Track
language:
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Author:

Poux-Médard Gaël^ORCID,Velcin Julien^ORCID,Loudcher Sabine^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43412-9_36

Reference33 articles.

1. Airoldi, E., Blei, D., Fienberg, S., Xing, E.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1991–1992 (2008)

2. Arratia, R., Barbour, A.D., Tavaré, S.: Poisson process approximations for the Ewens sampling formula. Ann. Appl. Probab. 2(3), 519–535 (1992)

3. Betancourt, B., Zanella, G., Miller, J.W., Wallach, H., Zaidi, A., Steorts, R.C.: Flexible models for microclustering with application to entity resolution, vol. 29 (2016). https://proceedings.neurips.cc/paper/2016/file/670e8a43b246801ca1eaca97b3e19189-Paper.pdf

4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

5. Blei, D., Frazier, P.: Distance dependent Chinese restaurant processes. J. Mach. Learn. Res. 12, 2461–2488 (2011)

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