﻿ 不同学习速率下NMF盲源分离算法

# 不同学习速率下NMF盲源分离算法Blind Source Separation Algorithms Based on Nonnegative Matrix Factorization Using Different Learning Rates

Abstract:
The iterative multipliable update formulas are used in blind source separation algorithms based on non-negative matrix factorization (NMF). However, the methods to select the learning rates and affect algorithms’ performance remain to be researched. This paper gives a derivation of different learning rates when selecting various iterative update formulas. A lot of computer simulations about these combinations are carried, and they show that a denominator of the effective iterative update formulas must contain information of the error function. In addition, its terms of denomi-nator and numerator should be balanced.

[1] Lee, D.D. and Seung, H.S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788-791.

[2] 李乐, 章毓晋 (2008) 非负矩阵分解算法综述. 电子学报, 36, 737-743.

[3] 马建仓, 牛奕龙, 陈海洋 (2006) 盲信号处理. 国防工业出版社, 北京.

[4] 殷海青, 刘红卫 (2010) 一种基于L1稀疏正则化和非负矩阵分解的盲源信号分离新算法. 西安电子科技大学学报, 37, 835-841.

[5] Zdunek, R. and Cichocki, A. (2007) Nonnegative matrix factorization with constrained second-order optimization. Signal Processing, 87, 1904-1916.
http://dx.doi.org/10.1016/j.sigpro.2007.01.024

[6] 张倩 (2013) 水声信号盲源分离方法研究. 硕士论文, 哈尔滨工业大学, 哈尔滨.

[7] 卢宏, 赵知劲, 杨小牛 (2011) 基于行列式和稀疏性约束的NMF的欠定盲分离方法. 计算机应用, 31, 553-555+558.

[8] Wang, S., et al. (2014) A K-L divergence constrained sparse NMF for hyperspectral unmixing signal. Proceedings of 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, Wuhan, 18-19 October 2014, 223-228.
http://dx.doi.org/10.1109/SPAC.2014.6982689

[9] 张宇飞 (2010) 加稀疏约束的非负矩阵分解. 硕士论文, 大连理工大学, 大连.

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