Corpus ID: 224802880

Wasserstein K-Means for Clustering Tomographic Projections

  title={Wasserstein K-Means for Clustering Tomographic Projections},
  author={Rohan Rao and Amit Moscovich and A. Singer},
  • Rohan Rao, Amit Moscovich, A. Singer
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • Motivated by the 2D class averaging problem in single-particle cryo-electron microscopy (cryo-EM), we present a k-means algorithm based on a rotationally-invariant Wasserstein metric for images. Unlike existing methods that are based on Euclidean ($L_2$) 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 $L_2… CONTINUE READING
    1 Citations

    Figures from this paper

    On the robustness of certain norms
    • W. Leeb
    • Computer Science, Mathematics
    • ArXiv
    • 2021
    • Highly Influenced
    • PDF


    Fast Computation of Wasserstein Barycenters
    • 393
    • PDF
    A clustering approach to multireference alignment of single-particle projections in electron microscopy.
    • 173
    Earthmover-Based Manifold Learning for Analyzing Molecular Conformation Spaces
    • 5
    • PDF
    Fast maximum-likelihood refinement of electron microscopy images
    • 57
    • PDF
    Reconstructing continuous distributions of 3D protein structure from cryo-EM images
    • 18
    • PDF
    Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
    • 72
    • PDF
    Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes
    • 21
    • PDF
    Unsupervised particle sorting for high-resolution single-particle cryo-EM
    • 4
    • PDF
    Computational Optimal Transport: With Applications to Data Science
    • 95
    • Highly Influential