CECM: Constrained evidential C-means algorithm
By Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux. 2012
95 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
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
- Kai Han
- Zsolt Kira
- Zhaoyang Lv
- Yen-Chang Hsu
- Joel Schlosser
- Phillip Odom
- Andrew Zisserman
- Andrea Vedaldi
In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
- Zhaoyang Lv
- Zsolt Kira
- Yen-Chang Hsu
- Joel Schlosser
- Phillip Odom
- Kai Han
- Andrew Zisserman
- Andrea Vedaldi
Citations in the corpus
- Learning to cluster in order to Transfer across domains and tasks (2017, neutral): “The third group uses constraints for both metric learning and the clustering objective, such as MPCKMeans (Bilenko et al., 2004) and CECM (Antoine et al., 2012).”
- Multi-class Classification without Multi-class Labels (2019, methodology): “There are also approaches combine both distance metric learning and a clustering objective jointly, such as MPCKMeans (Bilenko et al., 2004), CECM (Antoine et al., 2012), and Kullback–Leibler divergence based contrastive loss (KCL) (Hsu & Kira, 2016; Hsu et al., 2018).”