This paper presents a review about the usage of eigenvalues restrictions for constrained parameter estimation in mixtures of elliptical distributions according to the likelihood approach. The restrictions serve a twofold purpose: to avoid convergence to degenerate solutions and to reduce the onset of non interesting (spurious) local maximizers, related to complex likelihood surfaces. The paper shows how the constraints may play a key role in the theory of Euclidean data clustering. The aim here is to provide a reasoned survey of the constraints and their applications, considering the contributions of many authors and spanning the literature of the last 30 years.
Titolo: | Eigenvalues and constraints in mixture modeling: Geometric and computational issues | |
Autori interni: | ||
Data di pubblicazione: | 2018 | |
Rivista: | ||
Handle: | http://hdl.handle.net/20.500.11769/359740 | |
Appare nelle tipologie: | 1.1 Articolo in rivista |