[1] LEE D, SEUNG H S. Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 1999, 401(6755): 788-791.
[2] LEE D, SEUNG H S. Algorithms for Non-negative Matrix Factorization[C/OL]. [2018-01-21]. http://www.cs.virginia.edu/~jdl/bib/nmf/lee00.pdf.
[3] TURK M, PENTLAND A. Eigen Faces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.
[4] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[5] HE X F, YAN S C, HU Y X, et al. Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[6] WANG Y X, ZHANG Y J. Nonnegative Matrix Factorization: A Comprehensive Review. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(6): 1336-1353.
[7] LUO P, PENG J Y, GUAN Z Y, et al. Dual Regularized Multi-view Non-negative Matrix Factorization for Clustering. Neurocomputing, 2017, 294: 1-11.
[8] KITAMURA D, ONN N, SAWADA H, et al. Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2016, 24(9): 1622-1637.
[9] XIAO Q, LUO J W, LIANG C, et al. A Graph Regularized Non-negative Matrix Factorization Method for Identifying MicroRNA-Di-sease Associations. Bioinformatics, 2018, 34(2): 239-248.
[10] HOYER P O. Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research, 2004, 5: 1457-1469.
[11] YUAN Z J, OJA E. Projective Nonnegative Matrix Factorization for Image Compression and Feature Extraction // Proc of the 14th Scandinavian Conference on Image Analysis. Berlin, Germany: Springer-Verlag, 2005: 333-342.
[12] ZHANG D Q, ZHOU Z H, CHEN S C. Non-negative Matrix Factorization on Kernels // Proc of the Pacific Rim International Conferences on Artificial Intelligence. Berlin, Germany: Springer-Verlag, 2006: 404-412.
[13] CAI D, HE X F, HAN J W, et al. Graph Regularized Nonnegative Matrix Factorization for Data Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1548-1560.
[14] LIU H F, WU Z H, CAI D, et al. Constrained Non-negative Matrix Factorization for Image Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1299-1311.
[15] 胡学考,孙福明,李豪杰.基于稀疏约束的半监督非负矩阵分解算法.计算机科学, 2015, 42(7): 280-284, 304.
(HU X K, SUN F M, LI H J. Constrained Nonnegative Matrix Factorization with Sparseness for Image Representation. Computer Science, 2015, 42(7): 280-284, 304.)
[16] 舒振球,赵春霞.基于局部学习的受限非负矩阵分解算法.华中科技大学学报(自然科学版), 2015, 43(7): 82-86.
(SHU Z Q, ZHAO C X. Constrained Nonnegative Matrix Factorization Based on Local Learning. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2015, 43(7): 82-86.)
[17] WANG Y, JIA Y D, HU C B, et al. Fisher Non-negative Matrix Factorization for Learning Local Features // Proc of the Asian Conferences on Computer Vision. Berlin, Germany: Springer-Verlag, 2004: 27-30.
[18] LU Y W, LAI Z H, XU Y, et al. Nonnegative Discriminant Matrix Factorization. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(7): 1392-1405.
[19] LIU Y H, JIA C C, LI B, et al. Gradient Descent Fisher Non-negative Matrix Factorization for Face Recognition. Journal of Information and Computational Science, 2013, 10(8): 2453-2461.
[20] LI P, BU J J, YANG Y, et al. Discriminative Orthogonal Nonnegative Matrix Factorization with Flexibility for Data Representation. Expert Systems with Applications, 2014, 41(4): 1283-1293.
[21] LOSING V, HAMMER B, WERSING H, et al. Incremental On-line Learning: A Review and Comparison of State of the Art Algorithms. Neurocomputing, 2017, 275: 1261-1274.
[22] HU C Y, CHEN Y Q, HU L S, et al. A Novel Random Forests Based Class Incremental Learning Method for Activity Recognition. Pattern Recognition, 2018, 78: 277-290.
[23] 郭虎升,王文剑,潘世超.基于组合半监督的增量支持向量机学习算法.模式识别与人工智能, 2016, 29(6): 504-510.
(GUO H S, WANG W J, PAN S C. Combinatorial Semi-supervised Incremental Support Vector Machine Learning Algorithm. Pattern Recognition and Artificial Intelligence, 2016, 29(6): 504-510.)
[24] BUCAK S S, GUNSEL B. Incremental Subspace Learning via Non-negative Matrix Factorization. Pattern Recognition, 2009, 42(5): 788-797.
[25] YU Z Z, LIU Y H, LI B, et al. Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition. Journal of Applied Mathematics, 2014. DOI: 10.1155/2014/928051.
[26] 孙 静,蔡希彪,姜小燕,等.基于图正则化和稀疏约束的增量型非负矩阵分解.计算机科学, 2017, 44(6): 298-305.
(SUN J, CAI X B, JIANG X Y, et al. Graph Regularized and Incremental Nonnegative Matrix Factorization with Sparseness Constraint. Computer Science, 2017, 44(6): 298-305.)
[27] WANG D, LU H C. On-line Learning Parts-Based Representation via Incremental Orthogonal Projective Non-negative Matrix Factorization. Signal Processing, 2013, 93(6): 1608-1623.
[28] 王海军,葛红娟.L0正则化增量正交投影非负矩阵分解的目标跟踪算法.系统工程与电子技术, 2016, 38(10): 2428-2434.
(WANG H J, GE H J. Object Tracking via Incremental Orthogonal Projective Non-negative Matrix Factorization with L0 Regularization. Systems Engineering and Electronics, 2016, 38(10): 2428-2434.)
[29] 王海军,葛红娟,张圣燕.在线增量正交投影非负矩阵分解的目标跟踪算法.江苏大学学报(自然科学版), 2016, 37(6): 698-705.
(WANG H J, GE H J, ZHANG S Y. Object Tracking Algorithm via Incremental Orthogonal Projective Non-negative Matrix Factorization. Journal of Jiangsu University(Natural Science Edition), 2016, 37(6): 698-705.)
[30] 王海军,张圣燕.基于L2范数和增量正交投影非负矩阵分解的目标跟踪算法.黑龙江大学自然科学学报, 2015, 32(2): 262-269.
(WANG H J, ZHANG S Y. Object Tracking Algorithm via L2 Norm and Incremental Orthogonal Projective Non-negative Matrix Factorization. Journal of Natural Science of Heilongjiang Univer-sity, 2015, 32(2): 262-269.)
[31] ZHU F, HONEINE P. Online Kernel Nonnegative Matrix Factorization. Signal Processing, 2017, 131: 143-153.
[32] 蔡 竞,王万良,郑建炜,等.增量式鉴别非负矩阵分解算法及其在人脸识别中的应用.图学学报, 2017, 38(5): 715-721.
(CAI J, WANG W L, ZHENG J W, et al. Incremental Discriminant Non-Negative Matrix Factorization and Its Application to Face Recognition. Journal of Graphics, 2017, 38(5): 715-721.) |