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.

Garcìa-escudero, L., Gordaliza, A., Greselin, F., Ingrassia, S., Mayo-iscar, A. (2018). Eigenvalues and constraints in mixture modeling: Geometric and computational issues. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 12(2), 203-233 [10.1007/s11634-017-0293-y].

Eigenvalues and constraints in mixture modeling: Geometric and computational issues

Greselin, F;
2018

Abstract

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.
Articolo in rivista - Articolo scientifico
Eigenvalues; EM algorithm; Mixture model; Model-based clustering;
Eigenvalues; EM algorithm; Mixture model; Model-based clustering; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics
English
23-ott-2017
2018
12
2
203
233
none
Garcìa-escudero, L., Gordaliza, A., Greselin, F., Ingrassia, S., Mayo-iscar, A. (2018). Eigenvalues and constraints in mixture modeling: Geometric and computational issues. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 12(2), 203-233 [10.1007/s11634-017-0293-y].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/175235
Citazioni
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 12
Social impact