Principal Component Compression Method for Covariance Matrices Used for Uncertainty Propagation

Autor:	Peter M. Harris, K. Ojasalo, David A. Humphreys, Dongsheng Zhao, Faisal Mubarak, Manuel Rodriguez-Higuero
Rok vydání:	2015
Předmět:	Propagation of uncertainty Estimation of covariance matrices Matrix (mathematics) Covariance function Covariance matrix Frequency domain Statistics Mathematical analysis Principal component analysis Electrical and Electronic Engineering Covariance Instrumentation Mathematics
Zdroj:	IEEE Transactions on Instrumentation and Measurement. 64:356-365
ISSN:	1557-9662 0018-9456
Popis:	We investigate a principal component analysis approach for compressing the covariance matrices derived from real-time and sampling oscilloscope measurements. The objective of reducing the data storage requirements to scale proportional to the trace length ${n}$ rather than ${n}^{2}$ is achieved, making the approach practical for representing results and uncertainties in either the time or frequency domain. Simulation results indicate that the covariance matrices can be represented in a compact form with negligible error. Mathematical manipulation of the compressed matrix can be achieved without the need to reconstruct the full covariance matrix. We have demonstrated compression of data sets containing up to 10 000 complex frequency components.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::86caf22ee441963874e5b41d844090ff https://doi.org/10.1109/tim.2014.2340640 Zobrazit plný text záznamu