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Active Data Selection for Fuzzy Topographic Mapping of Proximities
Citation key Hasenjaeger1999b
Author Hasenjäger, M. and Ritter, H. and Obermayer, K.
Title of Book Fuzzy-Neuro Systems 1999 - Computational Intelligence
Pages 93–104
Year 1999
ISSN 3-933240-75-1
Editor Brewka G. and Der R. and Gottwald S. and Schierwagen A.
Abstract We investigate algorithms for the grouping and for the topographic mapping of data items based on their mutual proximities. Given a full proximity matrix we first show that a large portion of its entries can be discarded without reducing the quality of the clustering solution. Although the clustering procedure is quite sensitive to the omission of large and small dissimilarity values, it is robust w.r.t. a deletion of up to 50\\% of the medium sized entries. These facts are then further exploited by actively selecting dissimilarity values for learning that are supposed to be most relevant. We describe a seleciton strategy which is based on maximizing the expected value of sample information. The advantage gained from active data selection depends on problem size as well as on the strength with which topography is imposed.
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