Efficient Greedy Learning of Gaussian Mixture Models
By Jakob Verbeek, Nikos A. Vlassis, Ben J. A. Kröse. Neural Computation, 2003
400 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/0c20cf7ce72d0df1e7a72ad4a94aed2a37c5a1c3
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
- Benedikt Pfülb
- Alexander Rainer Tassilo Gepperth
- Lionel Tarassenko
- Marco A. F. Pimentel
- Sylvain Calinon
- Ranjan Maitra
- Volodymyr Melnykov
- David A. Clifton
In Computer Vision and Pattern Recognition
- Alexander Rainer Tassilo Gepperth
- Lionel Tarassenko
- Sylvain Calinon
- Ranjan Maitra
- Volodymyr Melnykov
- David A. Clifton
- Benedikt Pfülb
- Sonia Chernova
Citations in the corpus
- A review of novelty detection (2014, methodology): “The Greedy EM clustering algorithm [123] is used to identify and initialise clusters corresponding to different operating modes.”
- Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces (2019, methodology): “Ap-proximating the full GMM log-likelihood is used in Verbeek et al. (2003); Pinheiro & Bates (1995); Dognin et al. (2009), although together with EM training.”
- A tutorial on task-parameterized movement learning and retrieval (2015, background): “Other forms of learning for mixture models are possible, including spectral clustering [68, 84, 52], online learning [67, 82, 27, 99, 34] or selfrefinement [19].”
- Finite mixture models and model-based clustering (2010, methodology): “Nevertheless, this method has been used in evaluating algorithms by [68, 115, 116].”