Posterior probabilistic clustering using NMF

ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings

Volumn , Issue , 2008, Pages 831-832

  Ding, Chris ^a   Luo, Dijun ^a   Li, Tao ^b   Peng, Wei ^b  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^b The Department of Electrical and Computer Engineering

Author keywords

NMF; Posterior probabilistic clustering; Sparse

Indexed keywords

NMF; NON-NEGATIVE MATRIX FACTORIZATIONS; POSTE-RIORS; POSTERIOR PROBABILISTIC CLUSTERING; POSTERIOR PROBABILITIES; PROBABILISTIC CLUSTERING; PROBABILISTIC LATENT SEMANTIC INDEXING; SPARSE;

BLIND SOURCE SEPARATION; FACTORIZATION; FLOW OF SOLIDS; INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; INFORMATION SERVICES; INFORMATION THEORY; PHOTOLITHOGRAPHY; RESEARCH AND DEVELOPMENT MANAGEMENT;

PROBABILITY;

EID: 57349129481     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390334.1390527     Document Type: Conference Paper

References (5)

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.