High-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model
Type
ArticleKAUST Department
Communication Theory LabComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering
Electrical Engineering Program
Date
2020Permanent link to this record
http://hdl.handle.net/10754/663879
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Quadratic 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.Citation
Sifaou, H., Kammoun, A., & Alouini, M.-S. (2020). High-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model. IEEE Access, 1–1. doi:10.1109/access.2020.3004812Journal
IEEE AccessAdditional Links
https://ieeexplore.ieee.org/document/9125879/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9125879
ae974a485f413a2113503eed53cd6c53
10.1109/ACCESS.2020.3004812
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