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Subspace-based spectrum estimation in innovation models by mixed norm minimization

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dc.contributor.authorAkçay, Hüseyin
dc.contributor.authorTürkay, Semiha
dc.contributor.orcid0000-0002-8831-2142
dc.contributor.orcid0000-0002-9370-5557
dc.date.accessioned2025-11-13T11:51:33Z
dc.date.issued2019-02-01
dc.identifier.doihttps://doi.org/10.1016/j.jfranklin.2019.01.042
dc.identifier.endpage3186
dc.identifier.issn0016-0032
dc.identifier.issue5
dc.identifier.openalexW2914276924
dc.identifier.startpage3169
dc.identifier.urihttps://hdl.handle.net/11421/8183
dc.identifier.urihttps://doi.org/10.1016/j.jfranklin.2019.01.042
dc.identifier.volume356
dc.language.isoen
dc.relation.ispartofJournal of the Franklin Institute
dc.rightsrestrictedAccess
dc.subjectSubspace topology
dc.subjectCovariance
dc.subjectMatrix norm
dc.subjectNorm (philosophy)
dc.subjectAlgorithm
dc.subjectMathematical optimization
dc.subjectMinification
dc.subjectSpectral density
dc.subjectCovariance matrix
dc.subjectDimension (graph theory)
dc.subjectMathematics
dc.subjectSystem identification
dc.subjectComputer science
dc.subjectEigenvalues and eigenvectors
dc.subjectData modeling
dc.subjectArtificial intelligence
dc.subjectStatistics
dc.subject.sdg9
dc.titleSubspace-based spectrum estimation in innovation models by mixed norm minimization
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5042802961
local.authorid.openalexA5042860530

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