Applications of Tensor Decompositions

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dc.contributor.authorTapia, Sergio Garcia
dc.contributor.authorHsu, Rebecca
dc.contributor.authorHu, Alyssa
dc.contributor.authorStevens II, Darren
dc.contributor.authorGraf, Jonathan S.
dc.contributor.authorGobbert, Matthias K.
dc.contributor.authorSimon, Tyler A.
dc.date.accessioned2018-09-21T18:59:39Z
dc.date.available2018-09-21T18:59:39Z
dc.date.issued2016
dc.description.abstractThis 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.en_US
dc.description.sponsorshipThese results were obtained as part of the REU Site: Interdisciplinary Program in High Performance Computing (hpcreu.umbc.edu) in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC) in Summer 2016. This program is funded by the National Science Foundation (NSF), the National Security Agency (NSA), and the Department of Defense (DOD), with additional support from UMBC, the Department of Mathematics and Statistics, the Center for Interdisciplinary Research and Consulting (CIRC), and the UMBC High Performance Computing Facility (HPCF). HPCF is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS{0821258 and CNS{1228778) and the SCREMS program (grant no. DMS{0821311), with additional substantial support from UMBC. Co-author Darren Stevens II was supported, in part, by the UMBC National Security Agency (NSA) Scholars Program through a contract with the NSA. Graduate assistant Jonathan Graf was supported by UMBC.en_US
dc.description.urihttps://userpages.umbc.edu/~gobbert/papers/REU2016Team7.pdfen_US
dc.format.extent18 pagesen_US
dc.genretechnical reporten_US
dc.identifierdoi:10.13016/M2B27PV50
dc.identifier.urihttp://hdl.handle.net/11603/11346
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofseriesHPCF Technical Report;HPCF-2016-17
dc.rightsThis 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.
dc.subjectTensorsen_US
dc.subjectTucker tensorsen_US
dc.subjectPrincipal component analysisen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleApplications of Tensor Decompositionsen_US
dc.typeTexten_US

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