[1] XU W, LIU X, GONG Y. Document clustering based on non-negative matrix factorization[C]//Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2003: 267-273. [2] WANG Y X, ZHANG Y J. Nonnegative matrix factorization: A comprehensive review[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 25(6): 1336-1353. [3] HE Y C, LU H T, HUANG L, et al. Non-negative matrix factorization with pairwise constraints and graph Laplacian[J]. Neural Processing Letters, 2015, 42(1): 167-185. [4] XU W, GONG Y. Document clustering by concept factorization[C]//Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004: 202-209. [5] CAI D, HE X, HAN J, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(8): 1548-1560. [6] CAI D, HE X, HAN J. Locally consistent concept factorization for document clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 23(6): 902-913. [7] CHEN Y, ZHANG J, CAI D, et al. Nonnegative local coordinate factorization for image representation[J]. IEEE Transactions on Image Processing, 2012, 22(3): 969-979. [8] LIU H, YANG Z, YANG J, et al. Local coordinate concept factorization for image representation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 25(6): 1071-1082. [9] 祁宏宇,吴小俊,王士同,杨静宇.一种协同的FCPM模糊聚类算法[J].模式识别与人工智能,2010,23(01):120-126. [10] 马文萍,黄媛媛,李豪,等. 基于粗糙集与差分免疫模糊聚类算法的图像分割[J]. 软件学报,2014,25(11):2675-2689. [11] 苏冬雪,吴小俊.基于多特征模糊聚类的图像融合方法[J].计算机辅助设计与图形学学报,2006,18(6):838-843. [12] YANG B, FU X, SIDIROPOULOS N D. Learning from hidden traits: Joint factor analysis and latent clustering[J]. IEEE Transactions on Signal Processing, 2016, 65(1): 256-269. [13] YU K, ZHANG T, GONG Y. Nonlinear learning using local coordinate coding[C]//Advances in Neural Information Processing Systems. 2009: 2223-2231. [14] NIE F, SHI S J, LI X. Semi-supervised learning with auto-weighting feature and adaptive graph[J]. IEEE Transactions on Knowledge and Data Engineering, 2019. [15] KYRILLIDIS A, BECKER S, CEVHER V, et al. Sparse projections onto the simplex[C]//International Conference on Machine Learning. 2013: 235-243. [16] NIE F, YANG S, ZHANG R, et al. A general framework for auto-weighted feature selection via global redundancyminimization[J]. IEEE Transactions on Image Processing, 2018, 28(5): 2428-2438. [17] CHEN X, YUAN G, NIE F, et al. Semi-supervised feature selection via sparse rescaled linear square regression[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 32(1): 165-176. [18] NIE F, HUANG H, CAI X, et al. Efficient and robust feature selection via joint 2, 1-norms minimization[C]//Advances in neural information processing systems. 2010: 1813-1821. [19] 沈浩,王士同.按风格划分数据的模糊聚类算法[J].模式识别与人工智能,2019,32(3):204-213. [20] SHI J, MALIK J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
() () |