| [1] Saito N, Larson B M, Benichou B. Sparsity vs Statistical Independence from a Best-basis Viewpoint [C] //Proc SPIE Wavelet Applications in Signal and Image Processing Ⅷ. USA: SPIE, 2000(4119): 474-486.
[2] 邱天爽, 毕晓辉. 稀疏分量分析在欠定盲源分离问题中的研究进展及应用[J] . 信号处理, 2008, 24(6): 966-970.
Qiu Tianshuang, Bi Xiaohui. Spares Component Analysis and Application for Underdetemined Blind Source Separation [J] . Signal Processing, 2008, 24(6): 966-970.
[3] Lee D D, Seung H S. Learning the Parts of Objects by Non-negative Matrix Factorization [J] . Nature, 1999(401): 788-791.
[4] 同鸣, 闫涛, 姬红兵. 一种抵抗强剪切攻击的鲁棒性数字水印算法 [J] . 西安电子科技大学学报, 2009, 36(1): 22-27.
Tong Ming, Yan Tao, Ji Hongbing. Strong Anti-robust Watermarking Algorithm[J] . Journal of Xidian University, 2009, 36(1): 22-27.
[5] 申丽岩, 方滨, 沈毅. 基于负熵极大的独立分量分析方法[J] . 中北大学学报, 2005, 26(6): 396-399.
Shen Liyan, Fang Bin, Shen Yi. An Independent Component Analysis Algorithm Based on Maximum Negentropy [J] . Journal of North China Institute of Technology, 2005, 26(6): 396-399.
[6] 吴微东, 庄哲民. 基于盲源分离的一种快速独立分量分析算法[J] . 汕头大学学报(自然科学版), 2004, 19(2): 58-61.
Wu Weidong, Zhuang Zhemi. An Algorithm of Linear-Mixed Signal Separation[J] . Journal of Shantou University (Natural Science Edition), 2004, 19(2): 58-61.
[7] 张军英, 刘利平. 基于部分独立分量分析的盲源分离[J] . 西安电子科技大学学报, 2004, 31(3): 334-337.
Zhang Junying, Liu Liping. Blind Signal Separation Based on the Partially Independent Component Analysis[J] . Journal of Xidian University, 2004, 31(3):334-337.
[8] 苏永振, 袁慎芳. 基于独立分量分析的多源冲击定位方法[J] . 振动与冲击, 2009, 28(8): 134-137.
Su Yongzhen, Yuan Shenfang. Method for Locating Multiple Impact Sources Based on Independent Component Analysis[J] . Journal of Vibration and Shock, 2009, 28(8): 134-137.
[9] Cichocki A, Zdunek R. Multilayer Nonnegative Matrix Factorization Using Projected Gradient Approaches [J] . International Journal of Neural Systems, 2007, 7(6): 431-446.
[10] Cichocki A, Zdunek R. Multilayer Nonnegative Matrix Factorization [J] . Electronics Letters, 2006, 42(16): 947-948.
[11] Cichocki A, Zdunek R. Regularized Alternating Least Squares Algorithms for Non-negative Matrix／tensor Factorizations [C] //Lecture Notes In Computer Science. Berlin: Springer, 2007(4493): 793-802.
[12] Zdunek R, Cichocki A. Nonnegative Matrix Factorization with Constrained Second-order Optimization [J] . Signal Processing, 2007(87): 1904-1916.
[13] Zdunek R, Cichocki A. Non-negative Matrix Factorization with Quasi-Newton Optimization [C] //The Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC). Berlin: Springer, 2006: 870-879.
[14] Donoho D L. Compressed Sensing [J] . IEEE Trans on Inform Theory, 2006(52): 1289-1306.
[15] Loris I. On the Performance of Algorithms for the Minimization of L1-penalized Functionals [J] . Inverse Problems, 2009, 25(3): 035008.
[16] Wright S, Nowak R, Figueiredo M. Sparse Reconstruction by Separable Approximation [J] . IEEE Trans on Signal Process, 2009, 57(7): 2479-2493.
[17] Lin C J. Projected Gradient Methods for Non-negative Matrix Factorization [J] . Neural Computation, 2007(19): 2756-2779.
[18] Barzilai J, Borwein J M. Two Point Step Size Gradient Method [J] . IMA J Numer Anal, 1988(8): 141-148.
[19] Birgin E G, Martínez J M, Raydan M. Nonmonotone Spectral Projected Gradient Methods on Convex Sets [J] . SIAM J Optim, 2000(10): 1196-1211.
[20] Kim D, Sra S, Dhillon I. A New Projected Quasi-Newton Approach for the Nonnegative Least Squares Problem [R] . Department of Computer Science, Austin, 12, 2006.
[21] Kim D, Sra S, Dhillon I S. Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem [C] //Proceedings of the 2007 SIAM Data Mining Conference. Minneapolis: SIAM, 2007.
[22] Cichocki A, Zdunek R. NMFLAB for Signal and Image Processing [R] . Laboratory for Advanced Brain Signal Processing, Saitama, 2006. |
