Abstract
Isotop is a new neural method for nonlinear projection ofhigh-dimensional data. Isotop builds the mapping between the data space and a projection space by means of topology preservation. Actually, the topology of the data to be projected is approximated by the use of neighborhoods between the neural units. Isotop is provided with a piecewise linear interpolator for the projection of generalization data after learning. Experiments on artificial and real data sets show the advantages of Isotop.
This work was realized with the support of the ‘Ministère de la Région wallonne’, under the ‘Programme de Formation et d’Impulsion à la Recherche Scientifique et Technologique’.
M.V. works as a senior research associate of the Belgian FNRS.
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Lee, J.A., Verleysen, M. (2002). Nonlinear Projection with the Isotop Method. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_151
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DOI: https://doi.org/10.1007/3-540-46084-5_151
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