WikiSysop: Created page with "== Citation == Rao, R.; Moscovich, A. & Singer, A. Wasserstein K-Means for Clustering Tomographic Projections. Proc. Machine Learning for Structural Biology Workshop, 202..."

2021-11-08T09:54:26Z

Created page with "== Citation == Rao, R.; Moscovich, A. & Singer, A. Wasserstein K-Means for Clustering Tomographic Projections. Proc. Machine Learning for Structural Biology Workshop, 202..."

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== Citation ==

Rao, R.; Moscovich, A. & Singer, A. Wasserstein K-Means for Clustering Tomographic Projections. Proc. Machine Learning for Structural Biology Workshop, 2021

== Abstract ==

Motivated by the 2D class averaging problem in single-particle cryo-electron microscopy, we present a k-means algorithm based on a rotationally-invariant Wasserstein metric for images. Unlike existing methods that are based on Euclidean (L2) distances, we prove that the Wasserstein metric better accommodates for the out-of-plane angular differences between different particle views. We demonstrate on a synthetic dataset that our method gives superior results compared to an L2 baseline. Furthermore, there is little computational overhead, thanks to the use of a fast linear-time approximation to the Wasserstein-1 metric, also known as the Earthmover’s distance.

== Keywords ==

== Links ==

https://www.mlsb.io/papers/MLSB2020_Wasserstein_K-Means_for_Clustering.pdf

== Related software ==

== Related methods ==

== Comments ==

2021Rao Wasserstein - Revision history

WikiSysop: Created page with "== Citation == Rao, R.; Moscovich, A. & Singer, A. Wasserstein K-Means for Clustering Tomographic Projections. Proc. Machine Learning for Structural Biology Workshop, 202..."