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

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.