.png){kind=icon}
返回
Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition
.png){kind=icon}
PRE
AI
DOI：10.1016/S0262-8856(02)00114-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper provides algorithms for adding and subtracting eigenspaces, thus allowing for incremental updating and downdating of data models. Importantly, and unlike previous work, we keep an accurate track of the mean of the data, which allows our methods to be used in classification applications. The result of adding eigenspaces, each made from a set of data, is an approximation to that which would obtain were the sets of data taken together. Subtracting eigenspaces yields a result approximating that which would obtain were a subset of data used. Using our algorithms, it is possible to perform 'arithmetic' on eigenspaces without reference to the original data. Eigenspaces can be constructed using either eigenvalue decomposition (EVD) or singular value decomposition (SVD). We provide addition operators for both methods, but subtraction for EVD only, arguing there is no closed-form solution for SVD. The methods and discussion surrounding SVD provide the principle novelty in this paper. We illustrate the use of our algorithms in three generic applications, including the dynamic construction of Gaussian mixture models. (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword:
singular value decomposition
eigenvalue decomposition
dynamic updating and downdating
Gaussian mixture models
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:
4.2
论文数:
4.1K
被引数:
6.7K
机构
暂无机构信息
引用论文
.png){kind=icon}
Serial 103.125-Gb/s Transmission over 1 km SSMF for Low-Cost, Short-Reach Optical Interconnects
PREAI
分享收藏 没有更多内容
没有更多内容