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Towards generic theory of data compression

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dc.contributor.authorTorokhti, A.
dc.contributor.authorFriedland, S.
dc.contributor.authorHowlett, P.G.
dc.contributor.conferenceIEEE International Symposium on Information Theory (24 Jun 2007 - 29 Jun 2007 : Nice, France)
dc.date.issued2007
dc.description.abstractIn this paper, we consider an extension and rigorous justification of Karhunen-Loève transform (KLT) which is an optimal technique for data compression. We propose and study the generic KLT which is treated as the best weighted linear estimator of a given rank under the condition that the associated covariance matrix is singular. As a result, the generic KLT is constructed in terms of the pseudo-inverse matrices that imply a development of the special technique. In particular, we give a solution of the new low-rank matrix approximation problem that provides a basis for the generic KLT. Theoretical aspects of the generic KLT are carefully studied. ©2007 IEEE.
dc.identifier.citation2007 IEEE international symposium on information theory, 2007, pp.291-295
dc.identifier.doi10.1109/ISIT.2007.4557241
dc.identifier.isbn9781424414291
dc.identifier.urihttps://hdl.handle.net/1959.8/48111
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUS
dc.rightsCopyright IEEE 2007
dc.source.urihttps://doi.org/10.1109/ISIT.2007.4557241
dc.subjectdata compression
dc.titleTowards generic theory of data compression
dc.typeConference paper
pubs.publication-statusPublished
ror.fileinfo12143367790001831 13143375880001831 9915911699301831_53109193310001831.pdf
ror.mmsid9915911699301831

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