Applications of Tensor Decompositions

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REU2016Team7.pdf (597.3 KB)

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https://userpages.umbc.edu/~gobbert/papers/REU2016Team7.pdf

Permanent Link

http://hdl.handle.net/11603/11346

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Mathematics and Statistics Department
UMBC Student Collection

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Author/Creator ORCID

Date

2016

Type of Work

technical report
Text

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Citation of Original Publication

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.

Subjects

Tensors
Tucker tensors
Principal component analysis
UMBC High Performance Computing Facility (HPCF)

Abstract

This report explores how data structures known as tensors can be used to perform multidimensional data analysis. If a matrix can be thought of as a two-dimensional array, then a tensor can be thought of as a multi-dimensional array (with more than two dimensions). Tensor decompositions are algorithms and tools that can allow the user to directly perform analysis on this type of data. After explaining the basics of tensors, we work with two di erent three-dimensional data sets and decompose the tensors in order to provide analysis and interpretations of various aspects of the data.