Mixtures of Factor Analyzers

By Geoffrey J. McLachlan, David Peel. 2000

246 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Signal Processing

How researchers see this paper

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

In Analytical Chemistry

1. Paul David McNicholas
2. Alexander Rainer Tassilo Gepperth
3. Benedikt Pfülb
4. Justin Paul
5. S. Bhakar

In Signal Processing

1. Paul David McNicholas
2. Alexander Rainer Tassilo Gepperth
3. Benedikt Pfülb
4. Justin Paul
5. S. Bhakar

Citations in the corpus

• A mixture model-based approach to the clustering of microarray expression data (2002, background): “…regularity conditions do not hold for the asymptotic null distribution of −2 log λ to be chi-squared; nor do they hold for the justification of the Bayesian Information Criterion (BIC), although it still appears to provide a useful informal guide in practice (McLachlan and Peel, 2000a, Chapter 6).”
• Image Modeling with Deep Convolutional Gaussian Mixture Models (2021, methodology): “Mixture of Factor Analyzers (MFAs) MFAs models [8], [21] can be considered as hierarchical GMMs because they are formulated in terms of a lower-dimensional latent-variable representation, which is mapped to a higher-dimensional space.”
• Model-Based Clustering (2016, methodology): “McLachlan and Peel (2000b) use the unconstrained mixture of factor analyzers, i.e., with Σg = ΛgΛ′g +Ψg.”
• Does Celebrity Image Congruence Influences Brand Attitude and Purchase Intention? (2018, methodology): “Analysis of the mediating/controlling effect of category variables in SEM models is possible as finite mixture modeling (Muthen, 2002 and McLachlan & Peel, 2000).”