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.
Thursday, October 18, 2012
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.