Improved Coresets for Euclidean $k$-Means

no code implementations • 15 Nov 2022 • Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar

the Euclidean $k$-median problem) consists of finding $k$ centers such that the sum of squared distances (resp.

An Empirical Evaluation of $k$-Means Coresets

1 code implementation • 3 Jul 2022 • Chris Schwiegelshohn, Omar Ali Sheikh-Omar

Using this benchmark and real-world data sets, we conduct an exhaustive evaluation of the most commonly used coreset algorithms from theory and practice.

Clustering

Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol

1 code implementation • arXiv 2020 • Lukas Hedegaard, Omar Ali Sheikh-Omar, Alexandros Iosifidis

Domain Adaptation is the process of alleviating distribution gaps between data from different domains.

Domain Adaptation Few-Shot Learning +1

Supervised Domain Adaptation using Graph Embedding

1 code implementation • 9 Mar 2020 • Lukas Hedegaard Morsing, Omar Ali Sheikh-Omar, Alexandros Iosifidis

Getting deep convolutional neural networks to perform well requires a large amount of training data.

Dimensionality Reduction Domain Adaptation +2