EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 48 (2022) ITM Web Conf., 48 (2022) 03006 References
Open Access
Issue
ITM Web Conf.
Volume 48, 2022
The 4th International Conference on Computing and Wireless Communication Systems (ICCWCS 2022)
Article Number 03006
Number of page(s) 5
Section Computer Science, Intelligent Systems and Information Technologies
DOI https://doi.org/10.1051/itmconf/20224803006
Published online 02 September 2022
  1. Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, and C´edric F´evotte. A quasi-newton algorithm on the orthogonal manifold for nmf with transform learning. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 700–704. IEEE, 2019. [CrossRef] [Google Scholar]
  2. Juan Jos´e Burred. Detailed derivation of multiplicative update rules for nmf. Paris, France, 2014. [Google Scholar]
  3. Yong Chen, Hui Zhang, Rui Liu, Zhiwen Ye, and Jianying Lin. Experimental explorations on short text topic mining between lda and nmf based schemes. Knowledge-Based Systems, 163:1–13, 2019. [CrossRef] [Google Scholar]
  4. Chen Cui, Xujun Wu, Jun Yang, and Juyan Li. A novel dibr 3d image hashing scheme based on pixel grouping and nmf. Wireless Communications and Mobile Computing, 2020, 2020. [Google Scholar]
  5. Flavia Esposito. A review on initialization methods for nonnegative matrix factorization: Towards omics data experiments. Mathematics, 9(9):1006, 2021. [Google Scholar]
  6. Yixian Fang, Huaxiang Zhang, and Yuwei Ren. Graph regularised sparse nmf factorisation for imagery denoising. IET Computer Vision, 12(4):466–475, 2018. [CrossRef] [Google Scholar]
  7. C´edric F´evotte, Emmanuel Vincent, and Alexey Ozerov. Single-channel audio source separation with nmf: divergences, constraints and algorithms. Audio Source Separation, pages 1–24, 2018. [Google Scholar]
  8. Yakui Huang, Hongwei Liu, and Shuisheng Zhou. Quadratic regularization projected barzilai–borwein method for nonnegative matrix factorization. Data mining and knowledge discovery, 29(6):1665–1684, 2015. [CrossRef] [MathSciNet] [Google Scholar]
  9. Yu Ito, Shin-ichi Oeda, and Kenji Yamanishi. Rank selection for non-negative matrix factorization with normalized maximum likelihood coding. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 720–728. SIAM, 2016. [CrossRef] [Google Scholar]
  10. Hyunsoo Kim and Haesun Park. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM journal on matrix analysis and applications, 30(2):713–730, 2008. [CrossRef] [MathSciNet] [Google Scholar]
  11. Daniel Lee and H Sebastian Seung. Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 13, 2000. [Google Scholar]
  12. Daniel D Lee and H Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788–791, 1999. [Google Scholar]
  13. Chih-Jen Lin. Projected gradient methods for nonnegative matrix factorization. Neural computation, 19(10):2756–2779, 2007. [CrossRef] [MathSciNet] [Google Scholar]
  14. Xianzhong Long, Hongtao Lu, Yong Peng, and Wenbin Li. Graph regularized discriminative non-negative matrix factorization for face recognition. Multimedia tools and applications, 72(3):2679–2699, 2014. [Google Scholar]
  15. Andri Mirzal. Nmf versus ica for blind source separation. Advances in Data Analysis and Classification, 11(1):25–48, 2017. [CrossRef] [MathSciNet] [Google Scholar]
  16. Laura Muzzarelli, Susanne Weis, Simon B Eickhoff, and Kaustubh R Patil. Rank selection in non-negative matrix factorization: systematic comparison and a new mad metric. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2019. [Google Scholar]
  17. Weiwei Pan and Finale Doshi-Velez. A characterization of the non-uniqueness of nonnegative matrix factorizations. arXiv preprint arXiv:1604.00653, 2016. [Google Scholar]
  18. Hanli Qiao. New svd based initialization strategy for non-negative matrix factorization. Pattern Recognition Letters, 63:71–77, 2015. [CrossRef] [Google Scholar]
  19. Yaser Esmaeili Salehani, Ehsan Arabnejad, Abderrahmane Rahiche, Athmane Bakhta, and Mohamed Cheriet. Msdb-nmf: Multispectral document image binarization framework via non-negative matrix factorization approach. IEEE Transactions on Image Processing, 29:9099–9112, 2020. [CrossRef] [MathSciNet] [Google Scholar]
  20. Steven Squires, Adam Pr¨ugel-Bennett, and Mahesan Niranjan. Rank selection in nonnegative matrix factorization using minimum description length. Neural computation, 29(8):2164–2176, 2017. [CrossRef] [MathSciNet] [Google Scholar]
  21. Ding Tu, Ling Chen, Mingqi Lv, Hongyu Shi, and Gencai Chen. Hierarchical online nmf for detecting and tracking topic hierarchies in a text stream. Pattern Recognition, 76:203–214, 2018. [CrossRef] [Google Scholar]
  22. Stefan Wild, James Curry, and Anne Dougherty. Improving non-negative matrix factorizations through structured initialization. Pattern recognition, 37(11):2217–2232, 2004. [Google Scholar]
  23. Yun Xue, Chong Sze Tong, Ying Chen, and Wen-Sheng Chen. Clustering-based initialization for non-negative matrix factorization. Applied Mathematics and Computation, 205(2):525–536, 2008. [Google Scholar]
  24. Xiaohui Yang, Wenming Wu, Xin Xin, Limin Su, and Liugen Xue. Adaptive factorization rank selection-based nmf and its application in tumor recognition. International Journal of Machine Learning and Cybernetics, pages 1–19, 2021. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.