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[1]刘伟强,林鹭*.基于不动点方程的非负矩阵分解算法[J].厦门大学学报(自然科学版),2018,57(04):526-531.[doi:10.6043/j.issn.0438-0479.201712022]
 LIU Weiqiang,LIN Lu*.Non-negative Matrix Factorization Algorithms Based on Fixed Point Equation[J].Journal of Xiamen University(Natural Science),2018,57(04):526-531.[doi:10.6043/j.issn.0438-0479.201712022]
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年04期
页码:
526-531
栏目:
研究论文
出版日期:
2018-07-31

文章信息/Info

Title:
Non-negative Matrix Factorization Algorithms Based on Fixed Point Equation
文章编号:
0438-0479(2018)04-0526-06
作者:
刘伟强林鹭*
厦门大学数学科学学院,福建 厦门 361005
Author(s):
LIU WeiqiangLIN Lu*
School of Mathematical Sciences,Xiamen University,Xiamen 361005,China
关键词:
非负矩阵分解 线性互补问题 不动点方程 最速下降法 最小梯度法
Keywords:
nonnegative matrix factorization linear complementarity problem fixed point equation steepest descent minimal gradient
分类号:
O 242
DOI:
10.6043/j.issn.0438-0479.201712022
文献标志码:
A
摘要:
从线性互补问题出发,通过非负矩阵分解问题与线性互补问题的关系,分别提出不动点方程的最速下降算法与最小梯度算法,证明了这两种算法的收敛性,并进行了数值实验.
Abstract:
Based on the linear complementarity problem,this paper proposes a steepest descent algorithm and a minimum gradient algorithm of the fixed point equation by the relationship between the NMF problem and the linear complementarity problem.The convergence of these two algorithms is proved and numerical simulations are carried out.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-12-14 录用日期:2018-03-21
基金项目:国家自然科学基金(51777177); 福建省客车及特种车辆研发协同创新中心项目(2016AYF002)
*通信作者:llin@xmu.edu.cn
引文格式:刘伟强,林鹭.基于不动点方程的非负矩阵分解算法[J].厦门大学学报(自然科学版),2018,57(4):526-531.
Citation:LIU W Q,LIN L.Non-negative matrix factorization algorithms based on fixed point equation[J].J Xiamen Univ Nat Sci,2018,57(4):526-531.(in Chinese)
更新日期/Last Update: 1900-01-01