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Please use this identifier to cite or link to this item: http://uvadoc.uva.es/handle/10324/32021
Title: Eigenvalues and constraints in mixture modeling: geometric and computational issues
Authors: García-Escudero, Luis Angel
Gordaliza, Alfonso
Greselin, Francesca
Salvatore, Ingrassia
Mayo-Iscar, Agustín
Issue Date: 2018
Citation: Advances in Data Analysis and Classification, Vol. 12, 203-233
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. These restrictions serve a twofold purpose: to avoid convergence to degenerate solutions and to reduce the onset of non interesting (spurious) 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 review of the constraints and their applications, along the contributions of many authors, spanning the literature of the last thirty years.
Peer Review: SI
Sponsor: Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18.
Language: spa
URI: http://uvadoc.uva.es/handle/10324/32021
Rights: info:eu-repo/semantics/openAccess
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Appears in Collections:DEP24 - Artículos de revista

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