A Component-Wise EM Algorithm for Mixtures

By Gilles Celeux, S. Chrétien, Florence Forbes, A. Mkhadri. 2001

176 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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. Miin-Shen Yang
2. Anil K. Jain
3. Chien-Yo Lai
4. Chih-Ying Lin
5. Ata Kabán
6. Jakramate Bootkrajang
7. Mário A. T. Figueiredo
8. Martin H. C. Law

In Statistics and Probability

1. Ata Kabán
2. Anil K. Jain
3. Miin-Shen Yang
4. Jakramate Bootkrajang
5. Chien-Yo Lai
6. Chih-Ying Lin
7. Mário A. T. Figueiredo
8. Martin H. C. Law

Citations in the corpus

• Learning kernel logistic regression in the presence of class label noise (2014, methodology): “The latter can be seen by noting that our algorithm is equivalent to a component-wise EM [10], that is an EM in which each component in the parameter space Θ = {w,Ω} is updated sequentially.”
• Simultaneous feature selection and clustering using mixture models (2004, background): “Because of this, a component-wise version of EM can be adopted [10], [18].”
• A robust EM clustering algorithm for Gaussian mixture models (2012, methodology): “For updating parameters, Figueiredo and Jain [20] used the component-wise EM method proposed by Celeux et al. [21], in which the parameters are sequentially updated.”