Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm
DOI10.1016/j.jmva.2012.02.012zbMath1352.62099OpenAlexW2018093048MaRDI QIDQ432292
Camille Brunet, Charles Bouveyron
Publication date: 4 July 2012
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2012.02.012
model-based clusteringconvergence propertieshigh-dimensional datadiscriminative subspaceFisher-EM algorithm
Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10)
Related Items (3)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
- Modelling high-dimensional data by mixtures of factor analyzers
- Dimensionally reduced mixtures of regression models
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- High-dimensional data clustering
- On the convergence properties of the EM algorithm
- A classification EM algorithm for clustering and two stochastic versions
- On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions
- Nuisance parameters, mixture models, and the efficiency of partial likelihood estimators
- An Optimal Set of Discriminant Vectors
- Heteroscedastic factor mixture analysis
This page was built for publication: Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm
