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dc.contributor.authorSifaou, Houssem
dc.contributor.authorKammoun, Abla
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2020-06-28T08:58:20Z
dc.date.available2020-06-28T08:58:20Z
dc.date.issued2020
dc.identifier.citationSifaou, H., Kammoun, A., & Alouini, M.-S. (2020). High-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model. IEEE Access, 1–1. doi:10.1109/access.2020.3004812
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2020.3004812
dc.identifier.urihttp://hdl.handle.net/10754/663879
dc.description.abstractQuadratic discriminant analysis (QDA) is a widely used classification technique that generalizes the linear discriminant analysis (LDA) classifier to the case of distinct covariance matrices among classes. For the QDA classifier to yield high classification performance, an accurate estimation of the covariance matrices is required. Such a task becomes all the more challenging in high dimensional settings, wherein the number of observations is comparable with the feature dimension. A popular way to enhance the performance of QDA classifier under these circumstances is to regularize the covariance matrix, giving the name regularized QDA (R-QDA) to the corresponding classifier. In this work, we consider the case in which the population covariance matrix has a spiked covariance structure, a model that is often assumed in several applications. Building on the classical QDA, we propose a novel quadratic classification technique, the parameters of which are chosen such that the fisher-discriminant ratio is maximized. Numerical simulations show that the proposed classifier not only outperforms the classical R-QDA for both synthetic and real data but also requires lower computational complexity, making it suitable to high dimensional settings.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9125879/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9125879
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHigh-Dimensional Data
dc.subjectQuadratic Discriminant Analysis
dc.subjectRandom Matrix Theory
dc.subjectSpiked Covariance Models
dc.titleHigh-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model
dc.typeArticle
dc.contributor.departmentCommunication Theory Lab
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Access
dc.eprint.versionPublisher's Version/PDF
dc.identifier.pages1-1
kaust.personSifaou, Houssem
kaust.personKammoun, Abla
kaust.personAlouini, Mohamed-Slim
refterms.dateFOA2020-06-28T08:59:23Z


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