2018 Joint Mathematics Meeting (JMM18)

SIAM Invited Address, Minisymposium Coorganizer
San Diego, CA

Invited Address


Tensor Decomposition: A Mathematical Tool for Data Analysis


Tensors are multiway arrays, and tensor decompositions are powerful tools for data analysis. In this talk, we demonstrate the wide-ranging utility of the canonical polyadic (CP) tensor decomposition with examples in neuroscience and chemical detection. The CP model is extremely useful for interpretation, as we show with an example in neuroscience. However, it can be difficult to fit to real data for a variety of reasons. We present a novel randomized method for fitting the CP decomposition to dense data that is more scalable and robust than the standard techniques. We further consider the modeling assumptions for fitting tensor decompositions to data and explain alternative strategies for different statistical scenarios, resulting in a generalized CP tensor decomposition.

Date, Time, Location

  • Thursday, January 11, 2018, 11:10 a.m - 12:00 noon
  • Room 6AB, Upper Level, San Diego Convention Center

SIAM Minisymposium


Tensors! Mathematical Challenges and Opportunities


  • David Gleich, Purdue University
  • Tamara G. Kolda, Sandia National Laboratories
  • Luke Oeding, Auburn University

Date, Time, Location

  • Thursday January 11, 2018, 1:00 p.m. - 4:00 p.m.
  • Room 11A, Upper Level, San Diego Convention Center