148 citations · 1.2k across the 75 of their papers we have counts for
7 papers · 1 filter
Manifold learning with arbitrary norms
Joe Kileel, Amit Moscovich, Nathan Zelesko +1
Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality. Many o…
Wasserstein K-Means for Clustering Tomographic Projections
Rohan Rao, Amit Moscovich, Amit Singer
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 metr…
Product Manifold Learning
Sharon Zhang, Amit Moscovich, Amit Singer
We consider problems of dimensionality reduction and learning data representations for continuous spaces with two or more independent degrees of freedom. Such problems occur, for e…
Centering noisy images with application to cryo-EM
Ayelet Heimowitz, Nir Sharon, Amit Singer
We target the problem of estimating the center of mass of noisy 2-D images. We assume that the noise dominates the image, and thus many standard approaches are vulnerable to estima…
NMR Assignment through Linear Programming
Jose F. S. Bravo-Ferreira, David Cowburn, Yuehaw Khoo +1
Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used technique (after X-ray crystallography) for structural determination of proteins. A computational challenge in…
Super-resolution multi-reference alignment
Tamir Bendory, Ariel Jaffe, William Leeb +2
We study super-resolution multi-reference alignment, the problem of estimating a signal from many circularly shifted, down-sampled, and noisy observations. We focus on the low SNR…