Straight monotonic embedding of data sets in Euclidean spaces

Authors:
Pierre Courrieu
Affiliations:
Laboratoire de Psychologie Cognitive, CNRS-UMR 6146, Université de Provence, 29 avenue Robert Schuman, 13621 Aix-en-Provence Cedex 1, France
Venue:
Neural Networks
Year:
2002

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Abstract

This paper presents a fast incremental algorithm for embedding data sets belonging to various topological spaces in Euclidean spaces. This is useful for networks whose input consists of non-Euclidean (possibly non-numerical) data, for the on-line computation of spatial maps in autonomous agent navigation problems, and for building internal representations from empirical similarity data.