A Quantitative and Qualitative Analysis of Tensor Decompositions on Spatiotemporal Data
Tom Henretty, Muthu Baskaran, James Ezick, David Bruns-Smith, Tyler A. Simon
Publication Source: 2017 IEEE High Performance Extreme Computing Conference (HPEC '17), Waltham, MA, USA.
With the recent explosion of systems capable of generating and storing large quantities of GPS data, there is an opportunity to develop novel techniques for analyzing and gaining meaningful insights. In this paper we examine the application of tensor decompositions, a high-dimensional data analysis technique, to georeferenced data sets. Guidance is provided on fitting spatiotemporal data into the tensor model and analyzing the results. We find that tensor decompositions can provide insight and that future research into spatiotemporal tensor decompositions for pattern detection, clustering, and anomaly detection is warranted.