Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 02003
Number of page(s) 7
Section 3D Images Reconstruction and Virtual System
Published online 19 November 2018
  1. D D Lee, H S Seung. Learning the parts of objects by non-negative matrix factorization. NATURE. 401(1999) [Google Scholar]
  2. V P Pauca, J Piper. Nonnegative matrix factorization for spectral data analysis. Linear Algebra and its Applications. 416(2006) [Google Scholar]
  3. L D Miao, H H Qi, Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization. Geoscience and Remote Sensing, 45(2007) [Google Scholar]
  4. A Zymnis, S J Kim, J Skaf. Hyperspectral Image Unmixing via Alternating Projected Subgradients. Asilomar Conference on Signals, Systems & Computers ASILOMAR, 13(2007) [Google Scholar]
  5. S Jia and Y T Qian. Constrained nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Trans. Geosci. Remote Sens., 47(2009) [Google Scholar]
  6. S Q Du, Y Q Shi and W L Wang. Graph regularized-based semi-supervised non-negative matrix factorization. Computer Engineering and Applications, 48(2012) [Google Scholar]
  7. J J Liu, Z B Wu and Z H Wei. Spatial Correlation Constrained Sparse Representation for Hyperspectral Image Classification[J]. Journal of Electronics & Information Technology. 34(2012) [Google Scholar]
  8. B L Chen, M Li, and J X Wang. Disease gene identification by using graph kernels and Markov random fields[J]. Science China,11(2014) [Google Scholar]
  9. X Y Jiang, F M Sun and H J Li. Semi-supervised Nonnegative Matrix Factorization Based on Graph Regularization and Sparseness Constraints[J]. Computer Science, 43(2016) [Google Scholar]
  10. P L Dobrushin. The Description of a Random Field by Means of Conditional Probabilities and Conditions of Its Regularity, Theory of Probability and its Applications, 13 (1968) [Google Scholar]
  11. F Spitzer. Markov Random Fields and Gibbs Ensembles, The American Mathematical Monthly, 78(1971) [Google Scholar]
  12. H W Deng, D A Clausi. Unsupervised image segmentation using a simple MRF model with a new implementation scheme. Pattern Recognition, 37 (2004) [Google Scholar]
  13. D J M Bioucas, J M P Nascimento. Hyperspectral subspace identification . Geoscience and Remote Sensing, IEEE Transactions on, 46(2008) [Google Scholar]
  14. B Yuan. Research on blind processing algorithms of Hyperspectral unmixing[D]. The University of Chinese Academy of Sciences (2015) [Google Scholar]
  15. D D Lee. Algorithms for nonnegative matrix factorization[J]. Advances in Neural Information Processing Systems, 13(2001) [Google Scholar]
  16. J B Wu, D-N Commutative method and its convergence[J]. Journal on Numerical Methods and Computer Applications, 23(2002) [Google Scholar]

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