Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653573 | Neurocomputing | 2005 | 15 Pages |
Abstract
Here, an multidimensional scaling-based (MDS-based) topographic mapping algorithm is proposed, named the stochastic MDS network. Because this network utilizes not local but global information over all the units, it can find more optimal results than previous models. In addition, by using a stochastic gradient algorithm, the mapping formation in this network is carried out as efficiently as in SOM-like models based on only the local information. Some simple numerical experiments verified the validity and efficiency of this network. It was also applied to the formation of large-scale topographic mappings, and could form various interesting mappings.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yoshitatsu Matsuda, Kazunori Yamaguchi,