2021
Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise.
Landa B, Coifman RR, Kluger Y. Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise. SIAM Journal On Mathematics Of Data Science 2021, 3: 388-413. PMID: 34124607, PMCID: PMC8194191, DOI: 10.1137/20m1342124.Peer-Reviewed Original ResearchStochastic normalizationHeteroskedastic noiseGaussian kernelHigh-dimensional settingsMatrix convergesAmbient dimensionDifferent noise variancesEuclidean spaceData pointsNoise varianceSymmetric normalizationCertain normalizationAffinity matrixClean counterpartsPairwise distancesKernelNoiseData analysis techniqueSingle-cell RNA-sequencing dataParticular directionSpaceWidespread approachConvergesMatrixHeteroskedasticity
2009
Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
Singer A, Erban R, Kevrekidis IG, Coifman RR. Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. Proceedings Of The National Academy Of Sciences Of The United States Of America 2009, 106: 16090-16095. PMID: 19706457, PMCID: PMC2752552, DOI: 10.1073/pnas.0905547106.Peer-Reviewed Original ResearchConceptsStochastic dynamical systemsModel reduction approachHigh dimensional dynamic dataDynamical systemsNonlinear independent component analysisLocal principal component analysisSlow variablesMarkov matrixGood observablesDiffusion mapsNetwork simulationAnisotropic diffusionReduction approachData analysis techniqueAnalysis techniquesEigenvectorsDynamic dataObservablesIndependent component analysisComponent analysisSimulationsMatrix