44 citations · 98 across the 14 of their papers we have counts for
16 papers
Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density
Keyi Li, Yuval Kluger, Boris Landa
Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offe…
Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Keyi Li, Yuval Kluger, Boris Landa
Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are freque…
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Boris Landa, Yuval Kluger, Rong Ma
Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed mu…
The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery
Boris Landa, Yuval Kluger
Detecting and recovering a low-rank signal in a noisy data matrix is a fundamental task in data analysis. Typically, this task is addressed by inspecting and manipulating the spect…
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Boris Landa, Xiuyuan Cheng
The Gaussian kernel and its traditional normalizations (e.g., row-stochastic) are popular approaches for assessing similarities between data points. Yet, they can be inaccurate und…
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
Xiuyuan Cheng, Boris Landa
Bi-stochastic normalization provides an alternative normalization of graph Laplacians in graph-based data analysis and can be computed efficiently by Sinkhorn-Knopp (SK) iterations…
Biwhitening Reveals the Rank of a Count Matrix
Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger
Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA. Significant progress on this task w…
Scaling positive random matrices: concentration and asymptotic convergence
Boris Landa
It is well known that any positive matrix can be scaled to have prescribed row and column sums by multiplying its rows and columns by certain positive scaling factors (which are un…
Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
Boris Landa, Rihao Qu, Joseph Chang +1
Rejecting the null hypothesis in two-sample testing is a fundamental tool for scientific discovery. Yet, aside from concluding that two samples do not come from the same probabilit…
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
Boris Landa, Ronald R. Coifman, Yuval Kluger
A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean…
KLT Picker: Particle Picking Using Data-Driven Optimal Templates
Amitay Eldar, Boris Landa, Yoel Shkolnisky
Particle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challen…
Method of moments for 3-D single particle ab initio modeling with non-uniform distribution of viewing angles
Nir Sharon, Joe Kileel, Yuehaw Khoo +2
Single-particle reconstruction in cryo-electron microscopy (cryo-EM) is an increasingly popular technique for determining the 3-D structure of a molecule from several noisy 2-D pro…
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…
Multi-reference factor analysis: low-rank covariance estimation under unknown translations
Boris Landa, Yoel Shkolnisky
We consider the problem of estimating the covariance matrix of a random signal observed through unknown translations (modeled by cyclic shifts) and corrupted by noise. Solving this…
The steerable graph Laplacian and its application to filtering image data-sets
Boris Landa, Yoel Shkolnisky
In recent years, improvements in various image acquisition techniques gave rise to the need for adaptive processing methods, aimed particularly for large datasets corrupted by nois…
Steerable Principal Components for Space-Frequency Localized Images
Boris Landa, Yoel Shkolnisky
This paper describes a fast and accurate method for obtaining steerable principal components from a large dataset of images, assuming the images are well localized in space and fre…