arrow
Volume 30, Issue 1
An Effective Initialization for Orthogonal Nonnegative Matrix Factorization

Xuansheng Wang, Xiaoyao Xie & Linzhang Lu

J. Comp. Math., 30 (2012), pp. 34-46.

Published online: 2012-02

Export citation
  • Abstract

The orthogonal nonnegative matrix factorization (ONMF) has many applications in a variety of areas such as data mining, information processing and pattern recognition. In this paper, we propose a novel initialization method for the ONMF based on the Lanczos bidiagonalization and the nonnegative approximation of rank one matrix. Numerical experiments are given to show that our initialization strategy is effective and efficient.

  • AMS Subject Headings

65F99.

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JCM-30-34, author = {Xuansheng Wang, Xiaoyao Xie and Linzhang Lu}, title = {An Effective Initialization for Orthogonal Nonnegative Matrix Factorization}, journal = {Journal of Computational Mathematics}, year = {2012}, volume = {30}, number = {1}, pages = {34--46}, abstract = {

The orthogonal nonnegative matrix factorization (ONMF) has many applications in a variety of areas such as data mining, information processing and pattern recognition. In this paper, we propose a novel initialization method for the ONMF based on the Lanczos bidiagonalization and the nonnegative approximation of rank one matrix. Numerical experiments are given to show that our initialization strategy is effective and efficient.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.1110-m11si10}, url = {http://global-sci.org/intro/article_detail/jcm/8415.html} }
TY - JOUR T1 - An Effective Initialization for Orthogonal Nonnegative Matrix Factorization AU - Xuansheng Wang, Xiaoyao Xie & Linzhang Lu JO - Journal of Computational Mathematics VL - 1 SP - 34 EP - 46 PY - 2012 DA - 2012/02 SN - 30 DO - http://doi.org/10.4208/jcm.1110-m11si10 UR - https://global-sci.org/intro/article_detail/jcm/8415.html KW - Lanczos bidiagonalization, Orthogonal nonnegative matrix factorization, Low-rank approximation, Nonnegative approximation. AB -

The orthogonal nonnegative matrix factorization (ONMF) has many applications in a variety of areas such as data mining, information processing and pattern recognition. In this paper, we propose a novel initialization method for the ONMF based on the Lanczos bidiagonalization and the nonnegative approximation of rank one matrix. Numerical experiments are given to show that our initialization strategy is effective and efficient.

Xuansheng Wang, Xiaoyao Xie and Linzhang Lu. (2012). An Effective Initialization for Orthogonal Nonnegative Matrix Factorization. Journal of Computational Mathematics. 30 (1). 34-46. doi:10.4208/jcm.1110-m11si10
Copy to clipboard
The citation has been copied to your clipboard