Hypersphere Fitting From Noisy Data Using an EM Algorithm
Julien Lesouple, Barbara Pilastre, Yoann Altmann, Jean‐Yves Tourneret · IEEE Signal Processing Letters · 2021
This letter studies a new expectation maximization (EM) algorithm to solve the problem of circle, sphere and more generally hypersphere fitting. This algorithm relies on the introduction of random latent vectors having a priori independent von Mises-Fisher distributions defined on the hypersphere. This statistical model leads to a complete data likelihood whose expected value, conditioned on the observed data, has a Von Mises-Fisher distribution. As a result, the inference problem can be solved with a simple EM algorithm. The performance of the resulting hypersphere fitting algorithm is evaluated for circle and sphere fitting.