.png){kind=icon}
返回
Relative gradient speeding up additive updates for nonnegative matrix factorization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2004.01.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
There exist two kinds of iterative updates for nonnegative matrix factorization: additive and multiplicative. The former does not take into consideration the characteristic of the parameter space of the constrained optimization while the latter holds the nonnegativity well. The relative gradient has better convergence rate than the ordinary gradient, and has been successfully used for neural learning, especially for blind source separation and independent component analysis. This paper applies the relative gradient to speed up the additive updates for nonnegative matrix factorization according to square Euclidean error. The primary experiments on synthetic and real datasets demonstrate the effectiveness of the proposed method. (C) 2004 Elsevier B.V. All rights reserved.
Keyword:
nonnegative matrix factorization
relative gradient
additive updates
multiplicative updates
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
分享收藏New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms..png)
Heart
OAAI
HeartIF0
OAAI分享收藏分享收藏
查看更多内容