Identifying surfaces of low dimensions in high dimensional data

Agnar Høskuldsson

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    Abstract

    Methods are presented that find a nonlinear subspace in low dimensions that describe data given by many variables. The methods include nonlinear extensions of Principal Component Analysis and extensions of linear regression analysis. It is shown by examples that these methods give more reliable results than similar techniques, where variables are selected and polynomials in those used.
    Original languageEnglish
    Title of host publicationThe Expansion Method Symposium
    Place of PublicationOdense
    PublisherOdense University
    Publication date1996
    Publication statusPublished - 1996

    Cite this

    Høskuldsson, A. (1996). Identifying surfaces of low dimensions in high dimensional data. In The Expansion Method Symposium Odense University.