19 citations · 50 across the 10 of their papers we have counts for
15 papers
SDSR: A Spectral Divide-and-Conquer Approach for Species Tree Reconstruction
Ortal Reshef, Ofer Glassman, Or Zuk +3
Recovering a tree that represents the evolutionary history of a group of species is a key task in phylogenetics. Performing this task using sequence data from multiple genetic mark…
Moving Least Squares without Quasi-Uniformity: A Stochastic Approach
Shir Tapiro-Moshe, Yariv Aizenbud, Barak Sober
Local Polynomial Regression (LPR) and Moving Least Squares (MLS) are closely related nonparametric estimation methods, developed independently in statistics and approximation theor…
Estimation of Local Geometric Structure on Manifolds from Noisy Data
Yariv Aizenbud, Barak Sober
A common observation in data-driven applications is that high-dimensional data have a low intrinsic dimension, at least locally. In this work, we consider the problem of point esti…
Probabilistic Robust Autoencoders for Outlier Detection
Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger
Anomalies (or outliers) are prevalent in real-world empirical observations and potentially mask important underlying structures. Accurate identification of anomalous samples is cru…
Convergence rates of vector-valued local polynomial regression
Yariv Aizenbud, Barak Sober
Non-parametric estimation of functions as well as their derivatives by means of local-polynomial regression is a subject that was studied in the literature since the late 1970's. G…
Non-Parametric Estimation of Manifolds from Noisy Data
Yariv Aizenbud, Barak Sober
A common observation in data-driven applications is that high dimensional data has a low intrinsic dimension, at least locally. In this work, we consider the problem of estimating…
Spectral Top-Down Recovery of Latent Tree Models
Yariv Aizenbud, Ariel Jaffe, Meng Wang +5
Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying…
Approximating the Span of Principal Components via Iterative Least-Squares
Yariv Aizenbud, Barak Sober
In the course of the last century, Principal Component Analysis (PCA) have become one of the pillars of modern scientific methods. Although PCA is normally addressed as a statistic…
Rank-one Multi-Reference Factor Analysis
Yariv Aizenbud, Boris Landa, Yoel Shkolnisky
In recent years, there is a growing need for processing methods aimed at extracting useful information from large datasets. In many cases the challenge is to discover a low-dimensi…
Approximation of Functions over Manifolds: A Moving Least-Squares Approach
Barak Sober, Yariv Aizenbud, David Levin
We present an algorithm for approximating a function defined over a -dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise.…
Similarity Search Over Graphs Using Localized Spectral Analysis
Yariv Aizenbud, Amir Averbuch, Gil Shabat +1
This paper provides a new similarity detection algorithm. Given an input set of multi-dimensional data points, where each data point is assumed to be multi-dimensional, and an addi…
A max-cut approach to heterogeneity in cryo-electron microscopy
Yariv Aizenbud, Yoel Shkolnisky
The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to advancements in electron detectors' technology. Yet, one of the key open…
Matrix Decompositions using sub-Gaussian Random Matrices
Yariv Aizenbud, Amir Averbuch
In recent years, several algorithms, which approximate matrix decomposition, have been developed. These algorithms are based on metric conservation features for linear spaces of ra…
Randomized LU Decomposition Using Sparse Projections
Yariv Aizenbud, Gil Shabat, Amir Averbuch
A fast algorithm for the approximation of a low rank LU decomposition is presented. In order to achieve a low complexity, the algorithm uses sparse random projections combined with…
PCA-Based Out-of-Sample Extension for Dimensionality Reduction
Yariv Aizenbud, Amit Bermanis, Amir Averbuch
Dimensionality reduction methods are very common in the field of high dimensional data analysis. Typically, algorithms for dimensionality reduction are computationally expensive. T…