The EM Algorithm for Factor Analyzers:An Extension with Latent Variable

By Xingcai Zhou. 2006

86 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/51a9edf43551c3ed09ac52e25375593f3bf7612b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Paul David McNicholas
2. Charles Bouveyron
3. Camille Brunet
4. Ryan P. Browne
5. Thomas Brendan Murphy
6. Sylvain Calinon
7. J. Jacques
8. Benoît Frénay

Citations in the corpus

• Parsimonious Gaussian mixture models (2008, methodology): “These models are based on assuming a latent Gaussian model structure for each population; the latent Gaussian model is closely related to the mixture of factor analyzers model (Ghahramani and Hinton 1997).”
• Model-based clustering of high-dimensional data: A review (2014, methodology): “A general framework for the MFA model was also proposed by McNicholas & Murphy [69] which, in particular, includes the previous works of Ghahramani and Hinton and of McLachlan et al. [65].”
• Model-Based Clustering (2016, background): “Ghahramani and Hinton (1997) were the first to introduce a mixture of factor analyzers model.”
• A mixture of generalized hyperbolic distributions (2013, background): “There is also the possibility of considering a generalized hyperbolic analogue of the mixture of factor analyzers (Ghahramani and Hinton, 1997; McLachlan and Peel, 2000) or mixture of common factors (Baek et al.”