.png){kind=icon}
返回
An EM algorithm for learning sparse and overcomplete representations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2003.12.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments,enable the development of an EM algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
overcomplete representations
EM algorithm
maximum a posteriori
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Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏分享收藏 没有更多内容
没有更多内容