Open AccessLDRD final report : leveraging multi-way linkages on heterogeneous data.
Author(s) -
Daniel M. Dunlavy,
Tamara G. Kolda
Publication year - 2010
Language(s) - English
Resource type - Reports
DOI - 10.2172/1008126
Subject(s) - computer science , scalability , software , tensor (intrinsic definition) , factorization , data mining , missing data , data science , theoretical computer science , algorithm , database , machine learning , mathematics , programming language , pure mathematics
This report is a summary of the accomplishments of the 'Leveraging Multi-way Linkages on Heterogeneous Data' which ran from FY08 through FY10. The goal was to investigate scalable and robust methods for multi-way data analysis. We developed a new optimization-based method called CPOPT for fitting a particular type of tensor factorization to data; CPOPT was compared against existing methods and found to be more accurate than any faster method and faster than any equally accurate method. We extended this method to computing tensor factorizations for problems with incomplete data; our results show that you can recover scientifically meaningfully factorizations with large amounts of missing data (50% or more). The project has involved 5 members of the technical staff, 2 postdocs, and 1 summer intern. It has resulted in a total of 13 publications, 2 software releases, and over 30 presentations. Several follow-on projects have already begun, with more potential projects in development