DocumentCode
2285641
Title
EM algorithms for self-organizing maps
Author
Heskes, Tom ; Spanjers, Jan-Joost ; Wiegerinck, Wim
Author_Institution
RWCP Theor. Found., Nijmegen Univ., Netherlands
Volume
6
fYear
2000
fDate
2000
Firstpage
9
Abstract
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
Keywords
data visualisation; entropy; probability; self-organising feature maps; unsupervised learning; vector quantisation; elastic-net approach; expectation maximisation algorithms; high-dimensional data; mixture modeling; self-organizing maps; vector quantization; Annealing; Clustering algorithms; Data visualization; Entropy; Self organizing feature maps; Temperature; Topology; Unsupervised learning; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.859365
Filename
859365
Link To Document