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DD-HDS: A method for visualization and exploration of high-dimensional data.

Abstract : Mapping high-dimensional data in a low-dimensional space, for example, for visualization, is a problem of increasingly major concern in data analysis. This paper presents data-driven high-dimensional scaling (DD-HDS), a nonlinear mapping method that follows the line of multidimensional scaling (MDS) approach, based on the preservation of distances between pairs of data. It improves the performance of existing competitors with respect to the representation of high-dimensional data, in two ways. It introduces (1) a specific weighting of distances between data taking into account the concentration of measure phenomenon and (2) a symmetric handling of short distances in the original and output spaces, avoiding false neighbor representations while still allowing some necessary tears in the original distribution. More precisely, the weighting is set according to the effective distribution of distances in the data set, with the exception of a single user-defined parameter setting the tradeoff between local neighborhood preservation and global mapping. The optimization of the stress criterion designed for the mapping is realized by "force-directed placement" (FDP). The mappings of low- and high-dimensional data sets are presented as illustrations of the features and advantages of the proposed algorithm. The weighting function specific to high-dimensional data and the symmetric handling of short distances can be easily incorporated in most distance preservation-based nonlinear dimensionality reduction methods.
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Contributor : Sylvain Lespinats <>
Submitted on : Monday, February 11, 2008 - 10:24:15 AM
Last modification on : Tuesday, January 19, 2021 - 11:08:31 AM
Long-term archiving on: : Thursday, September 27, 2012 - 5:54:01 PM


  • HAL Id : inserm-00250168, version 1
  • PUBMED : 18220179


Sylvain Lespinats, Michel Verleysen, Alain Giron, Bernard Fertil. DD-HDS: A method for visualization and exploration of high-dimensional data.. IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers, 2007, 18 (5), pp.1265-79. ⟨inserm-00250168⟩



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