Best Symmetric Low Rank Approximation Via the Symmetry Preserving Singular Value Decomposition

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dc.contributor.authorShah, Mili I.
dc.contributor.authorSorensen, Danny C.
dc.date.accessioned2018-06-18T17:58:15Z
dc.date.available2018-06-18T17:58:15Z
dc.date.issued2007-12
dc.date.noteDecember 2007
dc.description.abstractThe symmetry preserving singular value decomposition (SPSVD) produces the best symmetric (low rank) approximation to a set of data. These symmetric approximations are characterized via an invariance under the action of a symmetry group on the set of data. The symmetry groups of interest consist of all the non-spherical symmetry groups in three dimensions. This set includes the rotational, reflectional, dihedral, and inversion symmetry groups. In order to calculate the best symmetric (low rank) approximation, the symmetry of the data set must be determined. Therefore, matrix representations for each of the non-spherical symmetry groups have been formulated. These new matrix representations lead directly to a novel reweighting iterative method to determine the symmetry of a given data set by solving a series of minimization problems. Once the symmetry of the data set is found, the best symmetric (low rank) approximation in the Frobenius norm and matrix 2-norm can be established by using the SPSVD.
dc.format.extent11 pp
dc.identifier.citationShah, Mili I. and Sorensen, Danny C.. "Best Symmetric Low Rank Approximation Via the Symmetry Preserving Singular Value Decomposition." (2007) <a href="https://hdl.handle.net/1911/102080">https://hdl.handle.net/1911/102080</a>.
dc.identifier.digitalTR07-17
dc.identifier.urihttps://hdl.handle.net/1911/102080
dc.language.isoeng
dc.titleBest Symmetric Low Rank Approximation Via the Symmetry Preserving Singular Value Decomposition
dc.typeTechnical report
dc.type.dcmiText
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