2018
Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study
Dsilva C, Talmon R, Coifman R, Kevrekidis I. Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study. Applied And Computational Harmonic Analysis 2018, 44: 759-773. DOI: 10.1016/j.acha.2015.06.008.Peer-Reviewed Original ResearchNonlinear dynamical systemsDiffusion mapsLocal linear regressionNonlinear manifold learning algorithmDynamical systemsDynamical behaviorStochastic modelIntrinsic geometryEmbedding coordinatesSystem dimensionalitySynthetic data setsEigendirectionsComplex data setsParsimonious representationData setsSuch algorithmsManifold learning algorithmReal dataTrue dimensionalityManifold learningLearning algorithmAlgorithmCoordinatesDimensionalityManifold
2014
Diffusion maps for changing data
Coifman R, Hirn M. Diffusion maps for changing data. Applied And Computational Harmonic Analysis 2014, 36: 79-107. DOI: 10.1016/j.acha.2013.03.001.Peer-Reviewed Original ResearchParameter spaceDiffusion mapsHigh-dimensional dataLow-dimensional spaceApproximation theoremGraph LaplacianIntrinsic geometryDimensional spaceSet of parametersNonlinear mappingDimensional dataGlobal behaviorEmbedding changesSpaceTypes of dataTheoremPowerful toolLaplacianGraphGeometryTermsEmbeddingDistanceParameters
2013
Empirical intrinsic geometry for nonlinear modeling and time series filtering
Talmon R, Coifman RR. Empirical intrinsic geometry for nonlinear modeling and time series filtering. Proceedings Of The National Academy Of Sciences Of The United States Of America 2013, 110: 12535-12540. PMID: 23847205, PMCID: PMC3732962, DOI: 10.1073/pnas.1307298110.Peer-Reviewed Original ResearchIntrinsic geometryNon-Gaussian tracking problemsHigh-dimensional time seriesNonlinear filtering frameworkTime series filteringInformation geometryStochastic settingParametric manifoldTracking problemStatistical modelBayesian approachNonlinear modelingEmpirical distributionFiltering frameworkEmpirical dynamicsInstrumental modalitiesInferred modelGeometryTime seriesTime series analysisDifferent observationsReal signalsSeries analysisDynamicsAnalysis toolsNonlinear Modeling and Processing Using Empirical Intrinsic Geometry with Application to Biomedical Imaging
Talmon R, Shkolnisky Y, Coifman R. Nonlinear Modeling and Processing Using Empirical Intrinsic Geometry with Application to Biomedical Imaging. Lecture Notes In Computer Science 2013, 8085: 441-448. DOI: 10.1007/978-3-642-40020-9_48.Peer-Reviewed Original ResearchNonlinear filtering problemInformation geometryFiltering problemDifferential geometryNonlinear filteringIntrinsic modelingIntrinsic geometryBayesian frameworkStatistical modelRandom observationsNonlinear modelingInstrumental modalitiesInferred modelGeometryNoise resilientReal signalsInvariantsModelingPhoton counterModelBiomedical imagingFilteringApplicationsProblem
2007
Validation of low-dimensional models using Diffusion maps and Harmonic Averaging
Hagen G, Smith T, Banasuk A, Coifman R, Mezić I. Validation of low-dimensional models using Diffusion maps and Harmonic Averaging. 2007, 5353-5357. DOI: 10.1109/cdc.2007.4434821.Peer-Reviewed Original Research
2005
Comparison of Systems using Diffusion Maps
Vaidya U, Hagen G, Lafon S, Banaszuk A, Mezic I, Coifman R. Comparison of Systems using Diffusion Maps. 2005, 7931-7936. DOI: 10.1109/cdc.2005.1583444.Peer-Reviewed Original ResearchDiffusion mapsDynamical system modelWork of CoifmanSingular value decompositionPhase spaceLow-dimensional embeddingAcoustic oscillationsIntrinsic geometryQualitative behaviorDimensional embeddingCandidate modelsValue decompositionAssociated dynamicsData setsSystem modelComparison of systemsModel validationSpectral propertiesEfficient methodEigenvectorsCoifmanEt alGraphSimple metricLafon