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dc.contributor.authorElkhalil, Khalil
dc.contributor.authorKammoun, Abla
dc.contributor.authorCouillet, Romain
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2018-02-01T07:24:59Z
dc.date.available2018-02-01T07:24:59Z
dc.date.issued2017-12-13
dc.identifier.citationElkhalil K, Kammoun A, Couillet R, Al-Naffouri TY, Alouini M-S (2017) Asymptotic performance of regularized quadratic discriminant analysis based classifiers. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). Available: http://dx.doi.org/10.1109/MLSP.2017.8168172.
dc.identifier.doi10.1109/MLSP.2017.8168172
dc.identifier.urihttp://hdl.handle.net/10754/626960
dc.description.abstractThis paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that depends only on the covariances and means associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized QDA and can be used to determine the optimal regularization parameter that minimizes the misclassification error probability. Despite being valid only for Gaussian data, our theoretical findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from popular real data bases, thereby making an interesting connection between theory and practice.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8168172/
dc.subjectConvergence
dc.subjectCovariance matrices
dc.subjectError analysis
dc.subjectGaussian distribution
dc.subjectRandom variables
dc.subjectStandards
dc.subjectTraining
dc.titleAsymptotic performance of regularized quadratic discriminant analysis based classifiers
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journal2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
dc.contributor.institutionCNRS-Centrale Supelec-Universite Paris-Sud, France
kaust.personElkhalil, Khalil
kaust.personKammoun, Abla
kaust.personAl-Naffouri, Tareq Y.
kaust.personAlouini, Mohamed-Slim
dc.date.published-online2017-12-13
dc.date.published-print2017-09


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