2022
The Manifold Scattering Transform for High-Dimensional Point Cloud Data.
Chew J, Steach H, Viswanath S, Wu H, Hirn M, Needell D, Krishnaswamy S, Perlmutter M. The Manifold Scattering Transform for High-Dimensional Point Cloud Data. Proceedings Of Machine Learning Research 2022, 196: 67-78. PMID: 37159759, PMCID: PMC10164360.Peer-Reviewed Original ResearchDeep feature extractorDimensional point cloud dataPoint cloud dataHigh-dimensional point cloudsFeature extractorClassification taskCloud dataPoint cloudsLow-dimensional manifoldScattering transformSignal classificationPractical schemeDiffusion mapsInitial workNaturalistic systemExtractorDatasetCloudInvariance propertiesTransformTask
2019
Compressed Diffusion
Gigante S, Stanley J, Vu N, van Dijk D, Moon K, Wolf G, Krishnaswamy S. Compressed Diffusion. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030994.Peer-Reviewed Original ResearchData regionsModern data analysisDiffusion mapsMost kernel methodsDiffusion geometryHeavy computational loadData pointsRelated embeddingsKernel-based methodsCubic complexityDiffusion map embeddingBig datasetsCorrelation kernelLower dimensionSpectral embeddingComputational loadKernel methodDiffusion relationManifold learningLocal geometryDiffusion processEmbeddingTheoretical connectionsGeometryIntrinsic structure