Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.
[2]
Lee H, Battle A, Rainna R, et al. Efficient sparse coding algorithms[C]. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2006: 801-808.
[3]
Zheng M, Bu J J, Chen C, et al. Graph regularized sparse coding for image representation[J]. IEEE Trans on Image Processing, 2011, 20(5): 1327-1336.
[4]
Jiang Z L, Lin Z, Davis L S. Learning a discriminative dictionary for sparse coding via label consistent K-SVD[C]. IEEE Conf on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2011: 1697-1704.
[5]
Donoho D. For most large underdetermined systems of linear equations the minimal L1-norm solutions is also the sparsest solution[J]. Communications on Pure and Applied Mathemati, 2006, 59(6): 797-829.
[6]
Candes E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathemati, 2006, 59(8): 1207-1223.
[7]
Candes E, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies?[J]. IEEE Trans on Information Theory, 2006, 52(12): 5406-5425.
[8]
Cai D, Bao H J, He X F. Sparse concept coding for visual analysis[C]. IEEE Conf on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2011: 2905-2910.
[9]
Efron B, Hastie T, Johnstone I, et al. Least angle regression[J]. Annals of Statistics, 2004, 32(2): 407-499.
[10]
Cai D, He X F, Han J W. SRDA: An afficient algorithm for large-scale discriminant analysis[J]. IEEE Trans on Knowledge and Data Engineering, 2008, 20(1): 1-12.
[11]
Paige C C, Saunders M A. LSQR: An algorithm for sparse linear equations and sparse least squares[J]. ACM Transactions on Mathematical Software, 1982, 8(1): 43-71.
[12]
Wang Y X, Zhang Y J. Nonnegative matrix factorization: A comprehensive review[J]. IEEE Trans on Knowledge and Data Engineering, 2013, 25(6): 1336-1353.
[13]
Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization[C]. Proc of 2003 Int Conf Research and Development in Information Retrieval. New York: ACM Press, 2003: 267-273.