Title :
EM algorithms for self-organizing maps
Author :
Heskes, Tom ; Spanjers, Jan-Joost ; Wiegerinck, Wim
Author_Institution :
RWCP Theor. Found., Nijmegen Univ., Netherlands
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859365
