Cluster-weighted ...-factor analyzers for robust model-based clustering and dimension reduction

Cluster-weighted models represent a convenient approach for model-based clustering, especially when the covariates contribute to defining the cluster-structure of the data. However, applicability may be limited when the number of covariates is high and performance may be affected by noise and outlie...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Statistical methods & applications Jg. 24; H. 4; S. 623 - 649
Hauptverfasser: Subedi, Sanjeena, Punzo, Antonio, Ingrassia, Salvatore, Mcnicholas, Paul D
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Heidelberg Springer Nature B.V 01.11.2015
Schlagworte:
Algorithms
Analyzers
Clustering
Mathematical models
Maximization
Maximum likelihood estimation
Noise
Normal distribution
Texts
ISSN:1618-2510, 1613-981X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Cluster-weighted models represent a convenient approach for model-based clustering, especially when the covariates contribute to defining the cluster-structure of the data. However, applicability may be limited when the number of covariates is high and performance may be affected by noise and outliers. To overcome these problems, common/uncommon ...-factor analyzers for the covariates, and a ...-distribution for the response variable, are here assumed in each mixture component. A family of twenty parsimonious variants of this model is also presented and the alternating expectation-conditional maximization algorithm, for maximum likelihood estimation of the parameters of all models in the family, is described. Artificial and real data show that these models have very good clustering performance and that the algorithm is able to recover the parameters very well.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-015-0298-7