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Efficient and Robust Classification for Positive Definite Matrices with Wasserstein Metric


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dc.contributor.advisorZheng, Jingyi
dc.contributor.authorCui, Jian
dc.date.accessioned2021-04-16T13:05:59Z
dc.date.available2021-04-16T13:05:59Z
dc.date.issued2021-04-16
dc.identifier.urihttps://etd.auburn.edu//handle/10415/7655
dc.description.abstractRiemannian geometry methods are widely used to classify SPD (Symmetric Positives-Definite) matrices, such as covariances matrices of brain-computer interfaces. Common Riemannian geometry classification methods are based on Riemannian distance to compute the mean of matrices. The purpose of this paper is to propose different algorithms based on Bures-Wasserstein distance for computing the mean of SPD matrices. Combining two purposed BW algorithms, Inductive mean and Cheap mean, with the most common simple projection algorithm based on Riemannian distance, there are 6 kinds of mean algorithms tested. The results obtained in this paper include that Bures-Wasserstein simple projection mean algorithm has a better efficient and robust performance than the others.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectMathematics and Statisticsen_US
dc.titleEfficient and Robust Classification for Positive Definite Matrices with Wasserstein Metricen_US
dc.typeMaster's Thesisen_US
dc.embargo.lengthMONTHS_WITHHELD:24en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2023-04-16en_US
dc.contributor.committeeHuajun, Huang
dc.contributor.committeeZeng, Peng

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