Heteroscedastic factor mixture analysis

By Angela Montanari, Cinzia Viroli. 2010

65 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/616147f936168c5ff55b8f48a126ca55dc725ce8

How researchers see this paper

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

In Artificial Intelligence

1. Camille Brunet
2. Paul David McNicholas
3. Charles Bouveyron
4. Eric T. Nalisnick
5. Lars Hertel
6. Padhraic Smyth
7. Sylvain Calinon
8. David M. Blei

In Molecular Biology

1. Paul David McNicholas
2. Camille Brunet
3. Eric T. Nalisnick
4. Charles Bouveyron
5. Lars Hertel
6. Padhraic Smyth

Citations in the corpus

• Model-based clustering of high-dimensional data: A review (2014, methodology): “Still for the R software, the hmfa function [94] implements the approach proposed in [74], also based on the mixture of factor analyzers.”
• Approximate Inference for Deep Latent Gaussian Mixtures (2016, methodology): “Our deep Latent Gaussian mixture model (DLGMM) generalizes previous work such as Factor Mixture Analysis [12] and Deep Gaussian Mixtures [15] to arbitrary differentiable inter-layer transformations.”
• Model-Based Clustering (2016, background): “Much other work has been carried out around, and building on, the mixture of factor analyzers model (e.g., Galimberti, Montanari, and Viroli 2009; Viroli 2010; Montanari and Viroli 2010a, 2011).”