Search Results for author: Tal Shnitzer

Found 3 papers, 1 papers with code

ManiFeSt: Manifold-based Feature Selection for Small Data Sets

no code implementations18 Jul 2022 David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon

This in turn allows for the extraction of the hidden manifold underlying the features and avoids overfitting, facilitating few-sample FS.

Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets

1 code implementation3 Feb 2022 Tal Shnitzer, Mikhail Yurochkin, Kristjan Greenewald, Justin Solomon

We use manifold learning to compare the intrinsic geometric structures of different datasets by comparing their diffusion operators, symmetric positive-definite (SPD) matrices that relate to approximations of the continuous Laplace-Beltrami operator from discrete samples.

Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators

no code implementations21 Jan 2022 Tal Shnitzer, Hau-Tieng Wu, Ronen Talmon

Our approach combines three components that are often considered separately: (i) manifold learning for building operators representing the geometry of the variables, (ii) Riemannian geometry of symmetric positive-definite matrices for multiscale composition of operators corresponding to different time samples, and (iii) spectral analysis of the composite operators for extracting different dynamic modes.

Time Series

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