M-estimators of scatter with eigenvalue shrinkage

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorOllila, Esaen_US
dc.contributor.authorPalomar, Daniel P.en_US
dc.contributor.authorPascal, Frédéricen_US
dc.contributor.departmentDept Signal Process and Acousten_US
dc.contributor.departmentHong Kong University of Science and Technologyen_US
dc.contributor.departmentUniversité Paris-Saclayen_US
dc.date.accessioned2020-10-02T06:22:57Z
dc.date.available2020-10-02T06:22:57Z
dc.date.issued2020-05en_US
dc.description.abstractA popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data adaptive method to compute the optimal shrinkage parameter with minimum mean squared error is proposed. Our approach permits the use of any weight function such as Gaussian, Huber's, or t weight functions, all of which are commonly used in M-estimation framework. Our simulation examples illustrate that shrinkage M-estimators based on the proposed optimal tuning combined with robust weight function do not loose in performance to shrinkage SCM estimator when the data is Gaussian, but provide significantly improved performance when the data is sampled from a heavy-tailed distribution.en
dc.description.versionPeer revieweden
dc.format.extent5
dc.format.extent5305-5309
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationOllila , E , Palomar , D P & Pascal , F 2020 , M-estimators of scatter with eigenvalue shrinkage . in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings . , 9054555 , Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing , IEEE , pp. 5305-5309 , IEEE International Conference on Acoustics, Speech, and Signal Processing , Barcelona , Spain , 04/05/2020 . https://doi.org/10.1109/ICASSP40776.2020.9054555en
dc.identifier.doi10.1109/ICASSP40776.2020.9054555en_US
dc.identifier.isbn9781509066315
dc.identifier.issn1520-6149
dc.identifier.issn2379-190X
dc.identifier.otherPURE UUID: 3fd92695-e866-4847-817a-34409d5a7c35en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/3fd92695-e866-4847-817a-34409d5a7c35en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85091178249&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/51726928/Ollila_M_estimators_scatter_with_eigenvalue_shrinkage_ICASSP.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/46779
dc.identifier.urnURN:NBN:fi:aalto-202010025744
dc.language.isoenen
dc.relation.ispartofIEEE International Conference on Acoustics, Speech and Signal Processingen
dc.relation.ispartofseries2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedingsen
dc.relation.ispartofseriesProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processingen
dc.rightsopenAccessen
dc.subject.keywordElliptical distributionsen_US
dc.subject.keywordM-estimatorsen_US
dc.subject.keywordRegularizationen_US
dc.subject.keywordSample covariance matrixen_US
dc.subject.keywordShrinkageen_US
dc.titleM-estimators of scatter with eigenvalue shrinkageen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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