Growing neural gas efficiently
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00205386" target="_blank" >RIV/68407700:21230/13:00205386 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0925231212008351" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231212008351</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2012.10.004" target="_blank" >10.1016/j.neucom.2012.10.004</a>
Alternative languages
Result language
angličtina
Original language name
Growing neural gas efficiently
Original language description
This paper presents optimization techniques that substantially speed up the Growing Neural Gas (GNG) algorithm. The GNG is an example of the Self-Organizing Map algorithm that is a subject of an intensive research interest in recent years as it is used in various practical applications. However, a poor time performance on large scale problems requiring neural networks with a high amount of nodes can be a limiting factor for further applications (e.g., cluster analysis, classification, 3-D reconstruction) or a wider usage. We propose two optimization techniques that are aimed exclusively on an efficient implementation of the GNG algorithm internal structure rather than on a modification of the original algorithm. The proposed optimizations preserve allproperties of the GNG algorithm and enable to use it on large scale problems with reduced computational requirements in several orders of magnitude.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
—
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
—
Volume of the periodical
104
Issue of the periodical within the volume
March
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
72-82
UT code for WoS article
000316163500007
EID of the result in the Scopus database
—