Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LSP.2021.3051851 | IEEE Signal Processing Letters |
Keywords | DocType | Volume |
Hypersphere Fitting,Maximum Likelihood Estimation,Expectation-Maximization Algorithm,von Mises-Fisher distribution | Journal | 28 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julien Lesouple | 1 | 6 | 2.48 |
| Barbara Pilastre | 2 | 0 | 0.34 |
| Yoann Altmann | 3 | 229 | 22.58 |
| Jean-Yves Tourneret | 4 | 0 | 0.34 |