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

作者: 毛翊君 , 赵知劲 , 尚俊娜 :杭州电子科技大学通信工程学院,浙江 杭州;

关键词: 非负矩阵分解盲分离学习速率误差函数Non-Negative Matrix Factorization (NMF) Blind Source Separation (BSS) Learning Rates Error Function

摘要:
基于非负矩阵分解(NMF)的盲源分离算法采用乘性更新规则,但如何选择学习速率以及其对算法性能影响没有详细研究。对此,本文推导给出了选择不同学习速率时各种迭代更新公式,并对各种组合进行了大量计算机仿真实验,通过比较分析发现,有效的迭代更新公式的分母必须包含误差函数信息,分子分母的项数应尽可能平衡。

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.

文章引用: 毛翊君 , 赵知劲 , 尚俊娜 (2015) 不同学习速率下NMF盲源分离算法。 无线通信, 5, 91-97. doi: 10.12677/HJWC.2015.55013

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