Improving Performance and Accuracy of Local PCA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10099306" target="_blank" >RIV/00216208:11320/11:10099306 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/11:00183691

Výsledek na webu

<a href="http://dx.doi.org/10.1111/j.1467-8659.2011.02047.x" target="_blank" >http://dx.doi.org/10.1111/j.1467-8659.2011.02047.x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/j.1467-8659.2011.02047.x" target="_blank" >10.1111/j.1467-8659.2011.02047.x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Performance and Accuracy of Local PCA

Popis výsledku v původním jazyce

Local Principal Component Analysis (LPCA) is one of the popular techniques for dimensionality reduction and data compression of large data sets encountered in computer graphics. The LPCA algorithm is a variant of k-means clustering where the repetitive classification of high dimensional data points to their nearest cluster leads to long execution times. The focus of this paper is on improving the efficiency and accuracy of LPCA. We propose a novel SortCluster LPCA algorithm that significantly reduces the cost of the point-cluster classification stage, achieving a speed-up of up to 20. To improve the approximation accuracy, we adopt the k-means++ algorithm [AV07]. We show that similar ideas that lead to the efficiency of our SortCluster LPCA algorithm can be used to accelerate k-means++. The resulting initialization algorithm is faster than purely random seeding while producing substantially more accurate data approximation.

Název v anglickém jazyce

Improving Performance and Accuracy of Local PCA

Popis výsledku anglicky

Local Principal Component Analysis (LPCA) is one of the popular techniques for dimensionality reduction and data compression of large data sets encountered in computer graphics. The LPCA algorithm is a variant of k-means clustering where the repetitive classification of high dimensional data points to their nearest cluster leads to long execution times. The focus of this paper is on improving the efficiency and accuracy of LPCA. We propose a novel SortCluster LPCA algorithm that significantly reduces the cost of the point-cluster classification stage, achieving a speed-up of up to 20. To improve the approximation accuracy, we adopt the k-means++ algorithm [AV07]. We show that similar ideas that lead to the efficiency of our SortCluster LPCA algorithm can be used to accelerate k-means++. The resulting initialization algorithm is faster than purely random seeding while producing substantially more accurate data approximation.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Computer Graphics Forum

ISSN

0167-7055

e-ISSN

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Svazek periodika

30

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

1903-1910

Kód UT WoS článku

000296915400005

EID výsledku v databázi Scopus

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