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EM Algorithms for Self-Organizing Maps
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859365IEEE-INNS-ENNS International Joint Co ...
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Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck, "EM Algorithms for Self-Organizing Maps," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 6, pp. 6009, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.859365,
author = {Tom Heskes and Jan-Joost Spanjers and Wim Wiegerinck},
title = {EM Algorithms for Self-Organizing Maps},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {6},
year = {2000},
issn = {1098-7576},
pages = {6009},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859365},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - EM Algorithms for Self-Organizing Maps
SN - 1098-7576
SP
EP
A1 - Tom Heskes,
A1 - Jan-Joost Spanjers,
A1 - Wim Wiegerinck,
PY - 2000
VL - 6
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Tom Heskes, University of Nijmegen
Jan-Joost Spanjers, University of Nijmegen
Wim Wiegerinck, University of Nijmegen
Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive EM algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional data. Several extensions and improvements are discussed.
Citation:
Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck, "EM Algorithms for Self-Organizing Maps," ijcnn, vol. 6, pp.6009, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000
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