An efficient MDS-based topographic mapping algorithm

Authors:
Yoshitatsu Matsuda;Kazunori Yamaguchi
Affiliations:
Kazunori Yamaguchi Laboratory, Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo 153-8902, Japan;Kazunori Yamaguchi Laboratory, Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo 153-8902, Japan
Venue:
Neurocomputing
Year:
2005

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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.