skip to main content
10.1145/1390334.1390527acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
poster

Posterior probabilistic clustering using NMF

Published: 20 July 2008 Publication History

Abstract

We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes the uncertainty in clustering assignment. Furthermore, PPC is closely related to probabilistic latent semantic indexing (PLSI).

References

[1]
C. Ding, X. He, and H.D. Simon. On the equivalence of nonnegative matrix factorization and spectral clustering. Proc. SIAM Data Mining Conf, 2005.
[2]
C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix t-factorizations for clustering. In KDD'06, pages 126--135, 2006.
[3]
T. Hofmann. Probabilistic latent semantic analysis. In ACM SIGIR-99, pages 289--296, 1999.
[4]
D.D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems, volume 13.
[5]
W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In Proc. ACM conf. Research and development in IR(SIGIR), pages 267--273, Toronto, Canada, 2003.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
July 2008
934 pages
ISBN:9781605581644
DOI:10.1145/1390334
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. NMF
  2. posterior probabilistic clustering
  3. sparse

Qualifiers

  • Poster

Conference

SIGIR '08
Sponsor:

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2015)CloudRecKnowledge and Information Systems10.1007/s10115-013-0723-x43:2(417-443)Online publication date: 1-May-2015
  • (2015)A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender SystemsAdvances in Information Retrieval10.1007/978-3-319-16354-3_38(346-351)Online publication date: 2015
  • (2014)An NMF-framework for Unifying Posterior Probabilistic Clustering and Probabilistic Latent Semantic IndexingCommunications in Statistics - Theory and Methods10.1080/03610926.2012.71403443:19(4011-4024)Online publication date: 23-May-2014
  • (2013)Relevance-based language modelling for recommender systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2013.03.00149:4(966-980)Online publication date: 1-Jul-2013
  • (2011)Extraction of Food Consumption Systems by Nonnegative Matrix Factorization (NMF) for the Assessment of Food ChoicesBiometrics10.1111/j.1541-0420.2011.01588.x67:4(1647-1658)Online publication date: 18-Mar-2011

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media