Search Results for author: Boris Landa

Found 8 papers, 3 papers with code

Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling

no code implementations16 Sep 2022 Boris Landa, Xiuyuan Cheng

In this work, we investigate this normalization in a setting where points are sampled from an unknown density on a low-dimensional manifold embedded in high-dimensional space and corrupted by possibly strong, non-identically distributed, sub-Gaussian noise.

Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise

1 code implementation22 Jun 2022 Xiuyuan Cheng, Boris Landa

This paper proves the convergence of the bi-stochastically normalized graph Laplacian to manifold (weighted-)Laplacian with rates when $n$ data points are i. i. d.

Scaling positive random matrices: concentration and asymptotic convergence

no code implementations11 Dec 2020 Boris Landa

Specifically, letting $\widetilde{A}\in\mathbb{R}^{M\times N}$ be a positive and bounded random matrix whose entries assume a certain type of independence, we provide a concentration inequality for the scaling factors of $\widetilde{A}$ around those of $A = \mathbb{E}[\widetilde{A}]$.

Probability Numerical Analysis Numerical Analysis 60B20, 60F10, 65F35,

Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise

no code implementations31 May 2020 Boris Landa, Ronald R. Coifman, Yuval Kluger

When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e. g. the row-stochastic normalization or its symmetric variant).

KLT Picker: Particle Picking Using Data-Driven Optimal Templates

1 code implementation12 Dec 2019 Amitay Eldar, Boris Landa, Yoel Shkolnisky

We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size.

Multi-reference factor analysis: low-rank covariance estimation under unknown translations

1 code implementation1 Jun 2019 Boris Landa, Yoel Shkolnisky

Solving this problem allows to discover low-rank structures masked by the existence of translations (which act as nuisance parameters), with direct application to Principal Components Analysis (PCA).

Statistics Theory Data Structures and Algorithms Information Theory Information Theory Statistics Theory

The steerable graph Laplacian and its application to filtering image data-sets

no code implementations6 Feb 2018 Boris Landa, Yoel Shkolnisky

Essentially, the steerable GL extends the standard GL by accounting for all (infinitely-many) planar rotations of all images.

Steerable Principal Components for Space-Frequency Localized Images

no code implementations9 Aug 2016 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 frequency.

Numerical Integration

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