Least square method robustness of computations: What is not usually considered and taught

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43952026" target="_blank" >RIV/49777513:23520/17:43952026 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.15439/2017F7" target="_blank" >http://dx.doi.org/10.15439/2017F7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15439/2017F7" target="_blank" >10.15439/2017F7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Least square method robustness of computations: What is not usually considered and taught

Popis výsledku v původním jazyce

There are many practical applications based on the Least Square Error (LSE) approximation. It is based on a square error minimization “on a vertical” axis. The LSE method is simple and easy also for analytical purposes. However, if data span is large over several magnitudes or non-linear LSE is used, severe numerical instability can be expected. The presented contribution describes a simple method for large span of data LSE computation. It is especially convenient if large span of data are to be processed, when the “standard” pseudoinverse matrix is ill conditioned. It is actually based on a LSE solution using orthogonal basis vectors instead of orthonormal basis vectors. The presented approach has been used for a linear regression as well as for approximation using radial basis functions.

Název v anglickém jazyce

Least square method robustness of computations: What is not usually considered and taught

Popis výsledku anglicky

There are many practical applications based on the Least Square Error (LSE) approximation. It is based on a square error minimization “on a vertical” axis. The LSE method is simple and easy also for analytical purposes. However, if data span is large over several magnitudes or non-linear LSE is used, severe numerical instability can be expected. The presented contribution describes a simple method for large span of data LSE computation. It is especially convenient if large span of data are to be processed, when the “standard” pseudoinverse matrix is ill conditioned. It is actually based on a LSE solution using orthogonal basis vectors instead of orthonormal basis vectors. The presented approach has been used for a linear regression as well as for approximation using radial basis functions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless metody pro vizualizaci velkých časově-prostorových vektorových dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Federated Conference on Computer Science and Information Systems,

ISBN

978-1-5090-4414-6

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

5

Strana od-do

537-541

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Praha

Datum konání akce

3. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000417412800080