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06/20/2023
The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery
Detecting and recovering a low-rank signal in a noisy data matrix is a f...
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09/16/2022
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
The Gaussian kernel and its traditional normalizations (e.g., row-stocha...
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06/22/2022
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
Bi-stochastic normalization of kernelized graph affinity matrix provides...
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03/25/2021
Biwhitening Reveals the Rank of a Count Matrix
Estimating the rank of a corrupted data matrix is an important task in d...
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12/11/2020
Scaling positive random matrices: concentration and asymptotic convergence
It is well known that any positive matrix can be scaled to have prescrib...
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11/06/2020
Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
Two-sample testing is a fundamental tool for scientific discovery. Yet, ...
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05/31/2020
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
A fundamental step in many data-analysis techniques is the construction ...
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12/12/2019
KLT Picker: Particle Picking Using Data-Driven Optimal Templates
Particle picking is currently a critical step in the cryo-EM single part...
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07/11/2019
Method of moments for 3-D single particle ab initio modeling with non-uniform distribution of viewing angles
Single-particle reconstruction in cryo-electron microscopy (cryo-EM) is ...
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06/01/2019
Multi-reference factor analysis: low-rank covariance estimation under unknown translations
We consider the problem of estimating the covariance matrix of a random ...
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05/29/2019
Rank-one Multi-Reference Factor Analysis
In recent years, there is a growing need for processing methods aimed at...
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02/06/2018
The steerable graph Laplacian and its application to filtering image data-sets
In recent years, improvements in various scientific image acquisition te...
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08/09/2016