High Dimensional and Large Span Data Least Square Error: Numerical Stability and Conditionality

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932668" target="_blank" >RIV/49777513:23520/17:43932668 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.17706/ijapm.2017.7.3.148-156" target="_blank" >http://dx.doi.org/10.17706/ijapm.2017.7.3.148-156</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17706/ijapm.2017.7.3.148-156" target="_blank" >10.17706/ijapm.2017.7.3.148-156</a>

Result language
angličtina
Original language name
High Dimensional and Large Span Data Least Square Error: Numerical Stability and Conditionality
Original language description
In this paper, we analyze influence of row and column “normalization” inspired by the Gershgorin’s theorem. The approach has been experimentally verified on a LSE application for high dimensional and large span data. The proposed approach was tested also on Hilbert’s matrix inversion for conditional number change analysis.
Czech name
—
Czech description
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Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless methods for large scattered spatio-temporal vector data visualization</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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