Robust Hypersphere Fitting from Noisy Data Using an EM Algorithm

Julien Lesouple, Barbara Pilastre, Yoann Altmann, Jean-Yves Tourneret

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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This article studies a robust expectation maximization (EM) algorithm to solve the problem of hypersphere fitting. This algorithm relies on the introduction of random latent vectors having independent von Mises-Fisher distributions defined on the hypersphere and random latent vectors indicating the presence of potential outliers. This model leads to an inference problem that can be solved with a simple EM algorithm. The performance of the resulting robust hypersphere fitting algorithm is evaluated for circle and sphere fitting with promising results in terms of both estimation performance and computation time.
Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021
Publication statusAccepted/In press - 4 May 2021
Event29th European Signal Processing Conference 2021 - Virtual, Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021


Conference29th European Signal Processing Conference 2021
Abbreviated titleEUSIPCO 2021


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