Principal Component Compression Method for Covariance Matrices Used for Uncertainty Propagation
| العنوان: | Principal Component Compression Method for Covariance Matrices Used for Uncertainty Propagation |
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| المؤلفون: | Peter M. Harris, K. Ojasalo, David A. Humphreys, Dongsheng Zhao, Faisal Mubarak, Manuel Rodriguez-Higuero |
| المصدر: | IEEE Transactions on Instrumentation and Measurement. 64:356-365 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2015. |
| سنة النشر: | 2015 |
| مصطلحات موضوعية: | 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 |
| الوصف: | 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. |
| تدمد: | 1557-9662 0018-9456 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::86caf22ee441963874e5b41d844090ff https://doi.org/10.1109/tim.2014.2340640 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........86caf22ee441963874e5b41d844090ff |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15579662 00189456 |
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