Tensor Analysis for Sparse Data

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CIS Colloquium, Dec 10, 2014, 11:00AM – 12:00PM, SERC 306

Tensor Analysis for Sparse Data

Tamara G. Kolda , Sandia National Laboratories

Tensors are higher-order or n-way arrays. They have proven useful in a wide variety of data analysis tasks in applications ranging from chemometrics to sociology to neuroscience, and much more. We discuss how tensor decomposition can be used to analyze sparse data coming from network science and other learning tasks. For instance, a time-evolving network can be naturally expressed as a third-order tensor. This talk explores the applicability of tensor analysis, its connection to matrix-based methods, as well as mathematical and computational considerations. We illustrate the utility of tensor decompositions with several examples.

Tamara (Tammy) Kolda is a Distinguished Member of Technical Staff in the Informatics and Systems Assessments department at Sandia National Laboratories in Livermore, California. Her research interests include multilinear algebra and tensor decompositions, graph models and algorithms, data mining, optimization, nonlinear solvers, parallel computing and the design of scientific software. Tamara has received SDM2013 and ICDM2008 Best Paper Prizes and a 2003 Presidential Early Career Award for Scientists and Engineers (PECASE). She currently serves on the SIAM Board of Trustees, as Section Editor for the Software and High Performance Computing Section of SISC, and as Associate Editor for SIMAX.