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
2019
Two-sample statistics based on anisotropic kernels
Cheng X, Cloninger A, Coifman RR. Two-sample statistics based on anisotropic kernels. Information And Inference A Journal Of The IMA 2019, 9: 677-719. PMID: 32929389, PMCID: PMC7478116, DOI: 10.1093/imaiai/iaz018.Peer-Reviewed Original Research
2015
Hölder–Lipschitz Norms and Their Duals on Spaces with Semigroups, with Applications to Earth Mover’s Distance
Leeb W, Coifman R. Hölder–Lipschitz Norms and Their Duals on Spaces with Semigroups, with Applications to Earth Mover’s Distance. Journal Of Fourier Analysis And Applications 2015, 22: 910-953. DOI: 10.1007/s00041-015-9439-5.Peer-Reviewed Original ResearchManifold Learning for Latent Variable Inference in Dynamical Systems
Talmon R, Mallat S, Zaveri H, Coifman R. Manifold Learning for Latent Variable Inference in Dynamical Systems. IEEE Transactions On Signal Processing 2015, 63: 3843-3856. DOI: 10.1109/tsp.2015.2432731.Peer-Reviewed Original ResearchDynamical systemsLatent variable inferenceOutput signal measurementsNonlinear observerEigenvector problemLaplace operatorSignal geometryIntrinsic distanceSignal measurementsAccurate recoveryIntrinsic variablesLatent variablesObserverInferenceMeasurement deviceManifoldOperatorsVariablesGeometryIntracranial electroencephalography signalsKernelDynamicsPropertiesProblemSolution
2013
Bi-stochastic kernels via asymmetric affinity functions
Coifman R, Hirn M. Bi-stochastic kernels via asymmetric affinity functions. Applied And Computational Harmonic Analysis 2013, 35: 177-180. DOI: 10.1016/j.acha.2013.01.001.Peer-Reviewed Original ResearchMultiscale data sampling and function extension
Bermanis A, Averbuch A, Coifman R. Multiscale data sampling and function extension. Applied And Computational Harmonic Analysis 2013, 34: 15-29. DOI: 10.1016/j.acha.2012.03.002.Peer-Reviewed Original ResearchSequence of approximationsGaussian kernel matrixData pointsAdaptive gridSpecial decompositionMultiscale schemeKernel matrixMultiscale decompositionGaussian kernelInterpolation methodMutual distanceData samplingFine hierarchyExtension methodEmpirical functionHierarchical procedureFunction extensionApproximationExtensionDecompositionPointKernelSchemeSubsamplingGrid
1990
Wavelets for the Fast Solution of Second-Kind Integral Equations
Alpert B, Beylkin G, Coifman R, Rokhlin V. Wavelets for the Fast Solution of Second-Kind Integral Equations. 1990 DOI: 10.21236/ada233650.Peer-Reviewed Original ResearchIntegral equationsSecond kind integral equationsKind integral equationsNon-oscillatory kernelsVector space basisIntegral operatorsNumerical solutionNumber of pointsFinite numberFast solutionSparse matricesNumerical resultsDiscretizationEquationsOperatorsSparse representationSingularitySolutionGeneralizationKernel