# 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.

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