A locally weighted sparse graph regularized Non-Negative Matrix Factorization method-Reference-Cited by-同舟云学术

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A locally weighted sparse graph regularized Non-Negative Matrix Factorization method

Published:2015-12 Issue: Volume:169 Page:68-76
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Feng Yinfu,Xiao Jun,Zhou Kang,Zhuang Yueting

Funder

National High Technology Research and Development Program

National Key Technology R&D Program

Zhejiang Provincial Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Zhejiang Province Public Technology Applied Research Projects

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference37 articles.

1. Learning the parts of objects by non-negative matrix factorization;Lee;Nature,1999

2. Principal component analysis;Wold;Chemom. Intell. Lab. Syst.,1987

3. I. Buciu, I. Pitas, Application of non-negative and local non negative matrix factorization to facial expression recognition, in: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2014), vol. 1, IEEE, 2004, pp. 288–291.

4. B. Wang, M. Pang, C. Lin, X. Fan, Graph regularized non-negative matrix factorization with sparse coding, in: IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), IEEE, 2013, pp. 476–480.

5. S. Behnke, Discovering hierarchical speech features using convolutional non-negative matrix factorization, in: Proceedings of the International Joint Conference on Neural Networks, vol. 4, IEEE, 2003, pp. 2758–2763.

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