44 citations · 98 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…