[1] LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization[C]//Advances in Neural Information Processing Systems, 2000.
[2] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.
[3] DING C, HE X F, SIMON H D. On the equivalence of nonnegative matrix factorization and spectral clustering[C]//Proceedings of the 2005 SIAM International Conference on Data Mining, 2005: 606-610.
[4] CAI D, HE X F, HAN J W, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(8): 1548-1560.
[5] 刘家骥, 包崇明, 周丽华, 等. 图正则化非负矩阵分解的异质网社区发现[J]. 计算机工程与应用, 2020, 56(21): 131-138.
LIU J J, BAO C M, ZHOU L H, et al. Community detecting method based on non-negative matrix factorization with graph regular term in heterogeneous information networks[J]. Computer Engineering and Applications, 2020, 56(21): 131-138.
[6] HUANG S D, XU Z L, KANG Z, et al. Regularized nonnegative matrix factorization with adaptive local structure learning[J]. Neurocomputing, 2020, 382: 196-209.
[7] LIU H F, WU Z H, LI X L, et al. Constrained nonnegative matrix factorization for image representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 34(7): 1299-1311.
[8] WANG D, GAO X B, WANG X M. Semi-supervised nonnegative matrix factorization via constraint propagation[J]. IEEE Transactions on Cybernetics, 2015, 46(1): 233-244.
[9] LI H R, GAO Y L, LIU J M, et al. Semi-supervised graph regularized nonnegative matrix factorization with local coordinate for image representation[J]. Signal Processing: Image Communication, 2022, 102: 116589.
[10] 刘兴建, 杨晓夫, 胡磊. 基于非负矩阵分解的半监督模型用于多层网络聚类[J]. 计算机与现代化, 2023(2): 83-88.
LIU X J, YANG X F, HU L. A semi-supervised model with non-negative matrix factorization for multiplex network clustering[J]. Computer and Modernization, 2023(2): 83-88.
[11] BABAEE M, TSOUKALAS S, BABAEE M, et al. Discriminative nonnegative matrix factorization for dimensionality reduction[J]. Neurocomputing, 2016, 173: 212-223.
[12] XING Z W, WEN M, PENG J G, et al. Discriminative semi-supervised non-negative matrix factorization for data clustering[J]. Engineering Applications of Artificial Intelligence, 2021, 103: 104289.
[13] PENG S Y, SER W, CHEN B, et al. Robust semi-supervised nonnegative matrix factorization for image clustering[J]. Pattern Recognition, 2021, 111: 107683.
[14] 高海燕, 刘万金, 黄恒君. 鲁棒自适应对称非负矩阵分解聚类算法[J]. 计算机应用研究, 2023, 40(4): 1024-1029.
GAO H Y, LIU W J, HUANG H J. Robust self-adaptived symmetric nonnegative matrix factorization clustering algorithm[J]. Application Research of Computers, 2023, 40(4): 1024-1029.
[15] KUANG D, DING C, PARK H. Symmetric nonnegative matrix factorization for graph clustering[C]//Proceedings of the 2012 SIAM International Conference on Data Mining, 2012: 106-117.
[16] KABIR K L, CHENNUPATI G, VANGARA R, et al. Decoy selection in protein structure determination via symmetric non-negative matrix factorization[C]//Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020: 23-28.
[17] MA Y Y, ZHAO J M, MA Y J. MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis[J]. BMC Bioinformatics, 2020, 21: 1-18.
[18] LUO X, LIU Z G, JIN L, et al. Symmetric nonnegative matrix factorization-based community detection models and their convergence analysis[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(3): 1203-1215.
[19] JIA Y H, LIU H, HOU J H, et al. Self-supervised symmetric nonnegative matrix factorization[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(7): 4526-4537.
[20] YANG L, CAO X C, JIN D, et al. A unified semi-supervised community detection framework using latent space graph regularization[J]. IEEE Transactions on Cybernetics, 2014, 45(11): 2585-2598.
[21] ZHANG X C, ZONG L L, LIU X Y, et al. Constrained clustering with nonnegative matrix factorization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 27(7): 1514-1526.
[22] LAN L, LIU T L, ZHANG X, et al. Label propagated nonnegative matrix factorization for clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(1): 340-351.
[23] LIU J M, WANG Y C, MA J, et al. Constrained nonnegative matrix factorization based on label propagation for data representation[J]. IEEE Transactions on Artificial Intelligence, 2024, 5(2): 590-601.
[24] HUANG D, WANG C D, LAI J H. Locally weighted ensemble clustering[J]. IEEE Transactions on Cybernetics, 2017, 48(5): 1460-1473.
[25] DIETTERICH T G. Ensemble methods in machine learning[C]//Proceedings of the International Workshop on Multiple Classifier Systems. Berlin, Heidelberg: Springer, 2000: 1-15.
[26] BIAN Y J, CHEN H H. When does diversity help generali-
zation in classification ensembles?[J]. IEEE Transactions on Cybernetics, 2021, 52(9): 9059-9075.
[27] FIGUEIREDO M A T, BIOUCAS-DIAS J M, NOWAK R D. Majorization-minimization algorithms for wavelet-based image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12): 2980-2991.
[28] SUN Y, BABU P, PALOMAR D P. Majorization-minimization algorithms in signal processing, communications, and machine learning[J]. IEEE Transactions on Signal Processing, 2016, 65(3): 794-816. |