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» Document clustering using nonnegative matrix factorization
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SIGIR
2005
ACM
14 years 2 months ago
Relation between PLSA and NMF and implications
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) have been successfully applied to a number of text analysis tasks such as docum...
Éric Gaussier, Cyril Goutte
CIKM
2008
Springer
13 years 10 months ago
Integrating clustering and multi-document summarization to improve document understanding
Document understanding techniques such as document clustering and multi-document summarization have been receiving much attention in recent years. Current document clustering meth...
Dingding Wang, Shenghuo Zhu, Tao Li, Yun Chi, Yiho...
ICASSP
2009
IEEE
13 years 6 months ago
Weighted nonnegative matrix factorization
Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity...
Yong-Deok Kim, Seungjin Choi
SIGIR
2004
ACM
14 years 2 months ago
GaP: a factor model for discrete data
We present a probabilistic model for a document corpus that combines many of the desirable features of previous models. The model is called “GaP” for Gamma-Poisson, the distri...
John F. Canny