Visual Pattern Discovery using Random Projections

Description: 

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 in- teresting 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 ran- dom projections. We define score functions, akin to projection pur- suit 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.

Authors: 
Anushka Anand
Tuan Nhon Dang
Leland Wilkinson
Publication Date: 
Thursday, October 18, 2012
Publication Information: 
Anushka Anand, Tuan Nhon Dang, and Leland Wilkinson. “Visual pattern discovery using random projections”. In Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology, 2012.