Data Mining and Knowledge Discovery

, Volume 26, Issue 3, pp 512–532 | Cite as

Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data

Article

Abstract

We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets.

Keywords

Dependence maps Dimensionality reduction Dependence Markov chain 

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Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Industrial EngineeringHanyang UniversitySeoulRepublic of Korea
  2. 2.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA

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