Spatial representation of dissimilarity data via lower-complexity linear and nonlinear mappings

EM Pekalska, RPW Duin

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    4 Citations (Scopus)


    Dissimilarity representations are of interest when it is hard to define well-discriminating features for the raw measurements. For an exploration of such data, the techniques of multidimensional scaling (MDS) can be used. Given a symmetric dissimilarity matrix, they find a lower-dimensional configuration such that the distances are preserved. Here, Sammon nonlinear mapping is considered. In general, this iterative method must be recomputed when new examples are introduced, but its complexity is quadratic in the number of objects in each iteration step. A simple modification to the nonlinear MDS, allowing for a significant reduction in complexity, is therefore considered, as well as a linear projection of the dissimilarity data. Now, generalization to new data can be achieved, which makes it suitable for solving classification problems. The linear and nonlinear mappings are then used in the setting of data visualization and classification. Our experiments show that the nonlinear mapping can be preferable for data inspection, while for discrimination purposes, a linear mapping can be recommended. Moreover, for the spatial lower-dimensional representation, a more global, linear classifier can be built, which outperforms the local nearest neighbor rule, traditionally applied to dissimilarities.
    Original languageUndefined/Unknown
    Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition, Proceedings
    EditorsT Caelli, A Amin, RPW Duin, M Kamel, D de Ridder
    Place of PublicationBerlin
    Number of pages9
    ISBN (Print)3-540-44011-9
    Publication statusPublished - 2002
    EventJoint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada) - Berlin
    Duration: 6 Aug 20029 Aug 2002

    Publication series

    PublisherSpringer Verlag
    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    ConferenceJoint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada)

    Bibliographical note

    ISSN 0302-9743, phpub 26


    • conference contrib. refereed
    • ZX CWTS JFIS < 1.00

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