EM algorithms for self-organizing maps

被引：3

作者：

Heskes, T ^{[1
]}

Spanjers, JJ ^{[1
]}

Wiegerinck, W ^{[1
]}

机构：

[1] Univ Nijmegen, SNN, RWCP, Theoret Fdn, NL-6252 EZ Nijmegen, Netherlands

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859365

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Ex.. ploiting the link between vector quantization and mixture modeling, we derive Ehl 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.

引用

页码：9 / 14

页数：6

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