.png){kind=icon}
返回
A fast and effective principal singular subspace tracking algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.06.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a fast and effective neural network algorithm to perform singular value decomposition (SVD) of a cross-covariance matrix between two high-dimensional data streams. Firstly, we derive a dynamical system from a newly proposed information criterion. This system exhibits a single stable stationary point if and only if the weight matrices of the left and right neural networks span the left and right principal singular subspace of a cross-covariance matrix, respectively, and the other stationary points are (unstable) saddle points. Then, a principal singular subspace (PSS) tracking algorithm is obtained from the dynamical system. Moreover, convergence analysis shows that the proposed algorithm converges to a stationary point that relates to the principal singular values. Thus, compared with traditional algorithms who can only track the PSS, the proposed algorithm can not only track the PSS but also estimate all of the corresponding principal singular values based on the extracted subspace. Finally, numerical simulations and practical application are carried to further demonstrate the efficiency of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Principal singular value
Principal singular subspace
Singular value decomposition
Cross-correlation neural network
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
引用论文
Symmetrical singular value decomposition representation for pattern recognition.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Cross-correlation neural network models for the smallest singular component of general matrixSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
查看更多内容