Title :
Using random projections to identify class-separating variables in high-dimensional spaces
Author :
Anand, Anushka ; Wilkinson, Leland ; Dang, Tuan Nhon
Author_Institution :
Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA
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.
Keywords :
data visualisation; statistical analysis; analytic visualization platform; class-separating variable ientification; class-separating views; high-dimensional spaces; noteworthy configuration recognition; projection pursuit; random projections; Cancer; Context modeling; Data mining; Data models; Electronic mail; Handwriting recognition; Visual analytics;
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-0015-5
DOI :
10.1109/VAST.2011.6102465
