摘要
在非负矩阵分解中,初值的选择对于算法效果有很大的影响.一些基于奇异值分解的初始化方法已有人提出[7,8],但当矩阵维数过大时,直接对原矩阵进行奇异值分解是耗时的.本文提出了一种更节时的初始化方法(KFV-NMF),而且通过数值实验,此算法既在一定程度上保持了计算精度,也节省了计算时间.
The selection of initial values is crucial for nonnegative matrix factorization(NMF),because it significantly influences the effectiveness of NMF algorithms.Some initialization methods based on singular value decomposition(SVD)have been proposed[7,8].However,when the dimension of the matrix is very large,it is time-consuming to compute the SVD of original matrix directly.In this paper,we propose a more time-saving initialization method(KFV-NMF).Numerical experiments show that our initialization algorithm needs less time and the accuracy is also maintained to some extent.
作者
陈红莉
CHEN Hong-li(School of Mathematics and Statistics,Wuhan University,Wuhan,430072,China)
出处
《数学杂志》
2020年第4期498-504,共7页
Journal of Mathematics
