Using random projections to identify class-separating variables in high-dimensional spaces

Anushka Anand, Leland Wilkinson, Tuan Nhon Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Projection Pursuit has been an effective method for finding interesting low-dimensional (usually 2D) projections in multidimensional spaces. Unfortunately, projection pursuit is not scalable to high-dimensional spaces. We introduce a novel method for approximating the results of projection pursuit to find class-separating views by using random projections. We build an analytic visualization platform based on this algorithm that is scalable to extremely large problems. Then, we discuss its extension to the recognition of other noteworthy configurations in high-dimensional spaces.

Original languageEnglish
Title of host publicationVAST 2011 - IEEE Conference on Visual Analytics Science and Technology 2011, Proceedings
Pages263-264
Number of pages2
DOIs
StatePublished - 2011
Event2nd IEEE Conference on Visual Analytics Science and Technology 2011, VAST 2011 - Providence, RI, United States
Duration: Oct 23 2011Oct 28 2011

Publication series

NameVAST 2011 - IEEE Conference on Visual Analytics Science and Technology 2011, Proceedings

Conference

Conference2nd IEEE Conference on Visual Analytics Science and Technology 2011, VAST 2011
CountryUnited States
CityProvidence, RI
Period10/23/1110/28/11

Keywords

  • H.5.2 [User Interfaces]: Graphical user interfaces (GUI) - [H.2.8]: Database Applications - Data Mining

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