The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering

  title={The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering},
  author={Tao Li and Chris H. Q. Ding},
  journal={Sixth International Conference on Data Mining (ICDM'06)},
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary… CONTINUE READING
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 575 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
135 Extracted Citations
35 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 135 extracted citations

575 Citations

Citations per Year
Semantic Scholar estimates that this publication has 575 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 35 references

Algorithms for non-negatvie matr ix factorization

  • D. Lee, H. S. Seung
  • Advances in Neural Information Processing Systems…
  • 2001
Highly Influential
4 Excerpts

and R

  • M. Berry, M. Browne, A. Langville, P. Pauca
  • Plemmons. Algorithms and applications for…
  • 2006
1 Excerpt

Accelarating ithe Lee-Seun g algorithms for nonnegative matrix factorization

  • E. F. Gonzales, Y. Zhang
  • Dept. of Comp. and Applied Math., Rice University…
  • 2005
1 Excerpt

Similar Papers

Loading similar papers…