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dc.contributor.authorAli, Hafiz Tiomoko
dc.contributor.authorKammoun, Abla
dc.contributor.authorCouillet, Romain
dc.date.accessioned2019-06-16T13:15:46Z
dc.date.available2019-06-16T13:15:46Z
dc.date.issued2018-09-07
dc.identifier.citationAli HT, Kammoun A, Couillet R (2018) Random Matrix-Improved Kernels For Large Dimensional Spectral Clustering. 2018 IEEE Statistical Signal Processing Workshop (SSP). Available: http://dx.doi.org/10.1109/SSP.2018.8450705.
dc.identifier.doi10.1109/SSP.2018.8450705
dc.identifier.urihttp://hdl.handle.net/10754/655594
dc.description.abstractLeveraging on recent random matrix advances in the performance analysis of kernel methods for classification and clustering, this article proposes a new family of kernel functions theoretically largely outperforming standard kernels in the context of asymptotically large and numerous datasets. These kernels are designed to discriminate statistical means and covariances across data classes at a theoretically minimal rate (with respect to data size). Applied to spectral clustering, we demonstrate the validity of our theoretical findings both on synthetic and real-world datasets (here, the popular MNIST database as well as EEG recordings on epileptic patients).
dc.description.sponsorshipThe work of R. Couillet and H. Tiomoko Ali is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8450705
dc.rightsArchived with thanks to 2018 IEEE Statistical Signal Processing Workshop (SSP)
dc.subjectinner product kernels
dc.subjectrandom matrix theory
dc.subjectSpectral clustering
dc.titleRandom Matrix-Improved Kernels For Large Dimensional Spectral Clustering
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journal2018 IEEE Statistical Signal Processing Workshop (SSP)
dc.conference.date2018-06-10 to 2018-06-13
dc.conference.name20th IEEE Statistical Signal Processing Workshop, SSP 2018
dc.conference.locationFreiburg im Breisgau, DEU
dc.eprint.versionPost-print
dc.contributor.institutionCentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, , , France
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
pubs.publication-statusPublished
kaust.personKammoun, Abla
refterms.dateFOA2019-06-16T13:15:47Z
dc.date.published-online2018-09-07
dc.date.published-print2018-06


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