Spherical k-Means++ Clustering

By Y. Endo, S. Miyamoto. 2015

233 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Optimization & Training, 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. Sabine Süsstrunk
2. Edo Collins
3. François Soumis
4. Yassine Yaakoubi
5. Simon Lacoste-Julien
6. Bob Price
7. Raja Bala
8. Peter Richtárik

In Information Systems

1. Simon Lacoste-Julien
2. Bob Price
3. Raja Bala
4. Sabine Süsstrunk
5. François Soumis
6. Yassine Yaakoubi
7. Edo Collins

In Optimization & Training

1. Simon Lacoste-Julien
2. Bob Price
3. Raja Bala
4. Sabine Süsstrunk
5. François Soumis
6. Yassine Yaakoubi
7. Edo Collins
8. Peter Richtárik

In Signal Processing

1. Sabine Süsstrunk
2. Edo Collins
3. Simon Lacoste-Julien
4. Bob Price
5. Raja Bala
6. François Soumis
7. Jaemyung Shin
8. Jian Jin

Citations in the corpus

• Structured Convolutional Kernel Networks for Airline Crew Scheduling (2021, methodology): “Then, we perform a spherical K -means algorithm (Buchta et al., 2012), acting as a Nyström approximation, to obtain p 1 centroids z 1 , . . . , z p 1 with unit (1) Consider all overlapping patches of I 0 .”
• Editing in Style: Uncovering the Local Semantics of GANs (2020, methodology): “Specifically, we applied spherical k-means clustering [5] to the C-dimensional activation vectors that make up the activation tensor A ∈”
• Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, methodology): “The iterative non-convex optimization for moving the cluster’s centre in K-means clustering algorithm is suitable for the data distributions supporting spherical clusters (Buchta et al., 2012).”