Improving K-Means by Outlier Removal

By Ville Hautamäki, S. Cherednichenko, Ismo Kärkkäinen, Tomi H. Kinnunen, Pasi Fränti. 2005

144 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, 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 Artificial Intelligence

1. Han-Jia Ye
2. De-chuan Zhan
3. Richard S. Zemel
4. Sergey Levine
5. Josh Tenenbaum
6. Yoshua Bengio
7. Trevor Darrell
8. Bernt Schiele

In Biomedical Engineering

1. Josh Tenenbaum
2. Richard S. Zemel

In Signal Processing

1. Hugo Larochelle
2. Kevin Swersky
3. Josh Tenenbaum
4. Richard S. Zemel
5. Eleni Triantafillou
6. Yoshua Bengio
7. Roland Memisevic
8. Ruslan Salakhutdinov

Citations in the corpus

• Meta-Learning for Semi-Supervised Few-Shot Classification (2018, methodology): “In addition to the original k -Means method (Lloyd, 1982), the most related work to our setup involving clustering algorithms considers applying k -Means in the presence of outliers (Hautamäki et al., 2005; Chawla & Gionis, 2013; Gupta et al., 2017).”