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Found 8 papers, 3 papers with code
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
The Gaussian kernel and its traditional normalizations (e. g., row-stochastic) are popular approaches for assessing similarities between data points.
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
This paper proves the convergence of 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
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
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
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
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
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
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.
