Visual pattern discovery using random projections

Anushka Anand, Leland Wilkinson, Tuan Nhon Dang

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

22 Scopus citations

Abstract

An essential element of exploratory data analysis is the use of revealing low-dimensional projections of high-dimensional data. Projection Pursuit has been an effective method for finding interesting low-dimensional projections of multidimensional spaces by optimizing a score function called a projection pursuit index. However, the technique is not scalable to high-dimensional spaces. Here, we introduce a novel method for discovering noteworthy views of high-dimensional data spaces by using binning and random projections. We define score functions, akin to projection pursuit indices, that characterize visual patterns of the low-dimensional projections that constitute feature subspaces. We also describe an analytic, multivariate visualization platform based on this algorithm that is scalable to extremely large problems.

Original languageEnglish
Title of host publicationIEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings
Pages43-52
Number of pages10
DOIs
StatePublished - 2012
Event2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012 - Seattle, WA, United States
Duration: Oct 14 2012Oct 19 2012

Publication series

NameIEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings

Conference

Conference2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012
CountryUnited States
CitySeattle, WA
Period10/14/1210/19/12

Keywords

  • High-dimensional Data
  • Random Projections

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