Search Results for author: Yariv Aizenbud

Found 8 papers, 3 papers with code

Non-Parametric Estimation of Manifolds from Noisy Data

1 code implementation11 May 2021 Yariv Aizenbud, Barak Sober

Assuming that the data was sampled uniformly from a tubular neighborhood of $\mathcal{M}\in \mathcal{C}^k$, a compact manifold without boundary, we present an algorithm that takes a point $r$ from the tubular neighborhood and outputs $\hat p_n\in \mathbb{R}^D$, and $\widehat{T_{\hat p_n}\mathcal{M}}$ an element in the Grassmanian $Gr(d, D)$.

Spectral Top-Down Recovery of Latent Tree Models

1 code implementation26 Feb 2021 Yariv Aizenbud, Ariel Jaffe, Meng Wang, Amber Hu, Noah Amsel, Boaz Nadler, Joseph T. Chang, Yuval Kluger

For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps.

Spectral neighbor joining for reconstruction of latent tree models

3 code implementations28 Feb 2020 Ariel Jaffe, Noah Amsel, Yariv Aizenbud, Boaz Nadler, Joseph T. Chang, Yuval Kluger

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model.

Approximation of Functions over Manifolds: A Moving Least-Squares Approach

no code implementations2 Nov 2017 Barak Sober, Yariv Aizenbud, David Levin

The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold.

Dimensionality Reduction

A max-cut approach to heterogeneity in cryo-electron microscopy

no code implementations5 Sep 2016 Yariv Aizenbud, Yoel Shkolnisky

In this paper, we attempt to make the first steps towards rigorous mathematical analysis of the heterogeneity problem in cryo-electron microscopy.

Classification Electron Microscopy +1

Multi-View Kernel Consensus For Data Analysis

no code implementations28 Jun 2016 Moshe Salhov, Ofir Lindenbaum, Yariv Aizenbud, Avi Silberschatz, Yoel Shkolnisky, Amir Averbuch

Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.

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