Tensor decompositions

July 19 to July 23, 2004

at the

American Institute of Mathematics, San Jose, California

organized by

Gene H. Golub, Tamara G. Kolda, James G. Nagy, and Charles F. Van Loan

Original Announcement

This workshop will consider mathematical problems of tensor decomposition. Though higher-order tensor (also known as multidimensional, multi-way, or n-way array) decompositions have been around for more than three decades, the door is now opening on greater mathematical understanding and new applications. Previously, this topic has been the domain of researchers in psychometrics and chemometrics. Now, however, computationally oriented mathematicians have begun to take an interest and envision many more potential applications ranging from image and signal processing to data mining and more. The challenge is to find ways to extend these methods to larger data sets, i.e., data sets with thousands to millions of entries. This will require advances in the theory and computation of higher-order tensor decompositions.

The workshop will bring together researchers on this topic with specialists in scientific computing, linear algebra, and applications. The goal of the workshop is to develop the theoretical and computational tools necessary to tackle larger problems and new applications. Some of the specific issues to be addressed are

Material from the workshop

A list of participants.

The workshop schedule.

A report on the workshop activities.

Reading List and Bibliography:
A reading list and bibliography, compiled by the organizers from participant contributions: dvi, postscript or pdf.


Links to Related Papers

Papers arising from the workshop:

A higher-order generalized singular value decomposition for comparison of global mRN expression from multiple organisms
by  Sri Priya Ponnapalli, Michael A. Saunders, Charles F. Van Loan, and Orly Alter,  PLoS ONE 6(12): e28072. (2011) doi:10.1371/journal.pone.0028072
Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA
by  Chaitanya Muralidhara, Andrew M. Gross, Robin R. Gutell, and Orly Alter