Non-Negative Matrix Factorization with Constraints

  title={Non-Negative Matrix Factorization with Constraints},
  author={Haifeng Liu and Zhaohui Wu},
Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation. However, NMF is essentially an unsupervised method and can not make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Non… CONTINUE READING
Highly Influential
This paper has highly influenced 10 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 51 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 31 extracted citations

52 Citations

Citations per Year
Semantic Scholar estimates that this publication has 52 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 20 references

Similar Papers

Loading similar papers…