Boris Landa
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View article: Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise Open
Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are frequently distorted by heteroskedastic noise$\unicode{x2014}$a p…
View article: Principled PCA separates signal from noise in omics count data
Principled PCA separates signal from noise in omics count data Open
Principal component analysis (PCA) is indispensable for processing high-throughput omics datasets, as it can extract meaningful biological variability while minimizing the influence of noise. However, the suitability of PCA is contingent o…
View article: Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets Open
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 multiple datasets from different studies or experimental cond…
View article: Gene trajectory inference for single-cell data by optimal transport metrics
Gene trajectory inference for single-cell data by optimal transport metrics Open
View article: Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling Open
.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 under high-dimensional noise, especially if the noise magnitu…
View article: The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery
The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery Open
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 spectrum of the observed data, e.g., thresholding the singular v…
View article: Biwhitening Reveals the Rank of a Count Matrix
Biwhitening Reveals the Rank of a Count Matrix Open
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 was achieved using random matrix theory by characterizing th…
View article: Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity
Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity Open
View article: Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling Open
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 under high-dimensional noise, especially if the noise magnitud…
View article: Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics
Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics Open
Single-cell RNA-sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell …
View article: Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise Open
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. This paper proves the convergence of bi-stochastically no…
View article: Decomposing a deterministic path to mesenchymal niche formation by two intersecting morphogen gradients
Decomposing a deterministic path to mesenchymal niche formation by two intersecting morphogen gradients Open
View article: Scaling positive random matrices: concentration and asymptotic convergence
Scaling positive random matrices: concentration and asymptotic convergence Open
It is well known that any positive matrix can be scaled to have prescribed\nrow and column sums by multiplying its rows and columns by certain positive\nscaling factors (which are unique up to a positive scalar). This procedure is\nknown a…
View article: Biwhitening Reveals the Rank of a Count Matrix
Biwhitening Reveals the Rank of a Count Matrix Open
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 was achieved using random matrix theory by characterizing th…
View article: Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise
Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise Open
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 space, a widespread approach is to from an affinity matrix …
View article: Rank-one multi-reference factor analysis
Rank-one multi-reference factor analysis Open
View article: Scaling positive random matrices: concentration and asymptotic convergence
Scaling positive random matrices: concentration and asymptotic convergence Open
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 unique up to a positive scalar). This procedure is known as m…
View article: Dual color mesoscopic imaging reveals spatiotemporally heterogeneous coordination of cholinergic and neocortical activity
Dual color mesoscopic imaging reveals spatiotemporally heterogeneous coordination of cholinergic and neocortical activity Open
Variation in an animal’s behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent control of circuit dynamics may reflect neuromodulatory influences including acetylcholine (ACh)…
View article: Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions Open
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 probability distribution, it is often of interest to characterize how…
View article: Multi-reference factor analysis: low-rank covariance estimation under unknown translations
Multi-reference factor analysis: low-rank covariance estimation under unknown translations Open
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 problem allows to discover low-rank structures masked by t…
View article: Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to\n Heteroskedastic Noise
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to\n Heteroskedastic Noise Open
A fundamental step in many data-analysis techniques is the construction of an\naffinity matrix describing similarities between data points. When the data\npoints reside in Euclidean space, a widespread approach is to from an affinity\nmatr…
View article: KLT picker: Particle picking using data-driven optimal templates
KLT picker: Particle picking using data-driven optimal templates Open
View article: The steerable graph Laplacian and its application to filtering image\n data-sets
The steerable graph Laplacian and its application to filtering image\n data-sets Open
In recent years, improvements in various image acquisition techniques gave\nrise to the need for adaptive processing methods, aimed particularly for large\ndatasets corrupted by noise and deformations. In this work, we consider\ndatasets o…
View article: The Steerable Graph Laplacian and its Application to Filtering Image Datasets
The Steerable Graph Laplacian and its Application to Filtering Image Datasets Open
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 noise and deformations. In this work, we consider datasets of i…