Search Results for author: Amit Moscovich

Found 9 papers, 6 papers with code

Manifold learning with arbitrary norms

1 code implementation28 Dec 2020 Joe Kileel, Amit Moscovich, Nathan Zelesko, Amit Singer

Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality.

Dimensionality Reduction

Wasserstein K-Means for Clustering Tomographic Projections

1 code implementation20 Oct 2020 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 metric for images.

Electron Microscopy

Product Manifold Learning

1 code implementation19 Oct 2020 Sharon Zhang, Amit Moscovich, Amit Singer

Mathematically, if the parameter space of each continuous independent motion is a manifold, then their combination is known as a product manifold.

Dimensionality Reduction Electron Microscopy

Unsupervised particle sorting for high-resolution single-particle cryo-EM

no code implementations22 Oct 2019 Ye Zhou, Amit Moscovich, Tamir Bendory, Alberto Bartesaghi

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies.

Electron Microscopy

Earthmover-based manifold learning for analyzing molecular conformation spaces

1 code implementation16 Oct 2019 Nathan Zelesko, Amit Moscovich, Joe Kileel, Amit Singer

In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction.

Dimensionality Reduction

Beyond Trees: Classification with Sparse Pairwise Dependencies

no code implementations6 Jun 2018 Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman

The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges.

Classification General Classification

Minimax-optimal semi-supervised regression on unknown manifolds

no code implementations7 Nov 2016 Amit Moscovich, Ariel Jaffe, Boaz Nadler

We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold.

Indoor Localization Pose Estimation

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