Publications

@article{8481477,

title = {Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion},

author = {James M Murphy and Mauro Maggioni},

doi = {10.1109/TGRS.2018.2869723},

issn = {1558-0644},

year  = {2019},

date = {2019-03-01},

journal = {IEEE Transactions on Geoscience and Remote Sensing},

volume = {57},

number = {3},

pages = {1829-1845},

keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral images, imaging, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{PathBasedSpectralClustering,

title = {Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms},

author = {Anna V Little and Mauro Maggioni and James M Murphy},

url = {http://jmlr.csail.mit.edu/papers/volume21/18-085/18-085.pdf},

year  = {2019},

date = {2019-01-01},

journal = {Journ. Mach. Learn. Res.},

volume = {21},

pages = {1-66},

keywords = {Clustering, diffusion geometry, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{2639-8001_2019_3_271,

title = {Learning by active nonlinear diffusion},

author = {Mauro Maggioni and James M Murphy},

url = {http://aimsciences.org//article/id/6f8fefb2-e464-48ea-b2de-f37686725966},

doi = {10.3934/fods.2019012},

issn = {A0000-0002},

year  = {2019},

date = {2019-01-01},

journal = {Foundations of Data Science},

volume = {1},

number = {"2639-8001-2019-3-271"},

pages = {271},

keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}

@article{CM:ATLAS,

title = {ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds},

author = {Miles C Crosskey and Mauro Maggioni},

url = {https://arxiv.org/abs/1404.0667

https://doi.org/10.1137/140970951},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Multiscale Modeling and Simulation},

volume = {15},

number = {1},

pages = {110--156},

note = {arxiv: 1404.0667},

keywords = {diffusion geometry, Machine learning, Manifold Learning, statistics, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{ZRMC:PolymerReversal,

title = {Polymer reversal rate calculated via locally scaled diffusion map},

author = {W Zheng and M A Rohrdanz and Mauro Maggioni and Cecilia Clementi},

year  = {2011},

date = {2011-01-01},

journal = {J. Chem. Phys.},

number = {134},

pages = {144108},

keywords = {diffusion geometry, Machine learning, Manifold Learning, molecular dynamics, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{RZMC:ReactionCoordinatesLocalScaling,

title = {Determination of reaction coordinates via locally scaled diffusion map},

author = {M A Rohrdanz and W Zheng and Mauro Maggioni and Cecilia Clementi},

year  = {2011},

date = {2011-01-01},

journal = {J. Chem. Phys.},

number = {134},

pages = {124116},

keywords = {diffusion geometry, Machine learning, Manifold Learning, molecular dynamics, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{jms:UniformizationEigenfunctions2,

title = {Universal local manifold parametrizations via heat kernels and eigenfunctions of the Laplacian},

author = {Peter W Jones and Mauro Maggioni and Raanan Schul},

year  = {2010},

date = {2010-01-01},

journal = {Ann. Acad. Scient. Fen.},

volume = {35},

pages = {1--44},

note = {http://arxiv.org/abs/0709.1975},

keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{SMC:GeneralFrameworkAdaptiveRegularization,

title = {Regularization on Graphs with Function-adapted Diffusion Processes},

author = {Arthur D Szlam and Mauro Maggioni and Ronald R Coifman},

year  = {2008},

date = {2008-08-01},

journal = {Jour. Mach. Learn. Res.},

number = {9},

pages = {1711--1739},

note = {(YALE/DCS/TR1365, Yale Univ, July 2006)},

keywords = {diffusion geometry, Machine learning, Manifold Learning, random walks, semisupervised learning, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{MM:DiffusionPolynomialFrames,

title = {Diffusion polynomial frames on metric measure spaces},

author = {Mauro Maggioni and Hrushikesh Mhaskar},

year  = {2008},

date = {2008-05-01},

journal = {ACHA},

volume = {3},

pages = {329--353},

keywords = {approximation theory, diffusion geometry, heat kernels, Laplacian eigenfunctions, multiscale analysis},

pubstate = {published},

tppubtype = {article}

}

@article{jms:UniformizationEigenfunctions,

title = {Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels},

author = {Peter W Jones and Mauro Maggioni and Raanan Schul},

year  = {2008},

date = {2008-02-01},

journal = {Proc. Nat. Acad. Sci.},

volume = {105},

number = {6},

pages = {1803--1808},

keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{CKLMN:DiffusionMapsReductionCoordinates,

title = {Diffusion Maps, reduction coordinates and low dimensional representation of stochastic systems},

author = {Ronald R Coifman and Ioannis G Kevrekidis and Stephane Lafon and Mauro Maggioni and Boaz Nadler},

year  = {2008},

date = {2008-01-01},

journal = {SIAM J.M.M.S.},

volume = {7},

number = {2},

pages = {842--864},

keywords = {diffusion geometry, dynamical systems, Laplacian eigenfunctions, Machine learning, model reduction, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{CM:MsDataDiffWavelets,

title = {Multiscale Data Analysis with Diffusion Wavelets},

author = {Ronald R Coifman and Mauro Maggioni},

year  = {2007},

date = {2007-04-01},

journal = {Proc. SIAM Bioinf. Workshop, Minneapolis},

keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{smmm:jmrl1,

title = {Proto-value Functions: A Spectral Framework for Solving Markov Decision Processes},

author = {Sridhar Mahadevan and Mauro Maggioni},

year  = {2007},

date = {2007-01-01},

journal = {JMLR},

volume = {8},

pages = {2169--2231},

keywords = {diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory},

pubstate = {published},

tppubtype = {article}

}

@article{CMDiffusionWavelets,

title = {Diffusion Wavelets},

author = {Ronald R Coifman and Mauro Maggioni},

year  = {2006},

date = {2006-07-01},

journal = {Appl. Comp. Harm. Anal.},

volume = {21},

number = {1},

pages = {53--94},

note = {(Tech. Rep. YALE/DCS/TR-1303, Yale Univ., Sep. 2004)},

keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{DiffusionWaveletPackets,

title = {Diffusion Wavelet Packets},

author = {James Jr. C Bremer and Ronald R Coifman and Mauro Maggioni and Arthur D Szlam},

year  = {2006},

date = {2006-07-01},

journal = {Appl. Comp. Harm. Anal.},

volume = {21},

number = {1},

pages = {95--112},

note = {(Tech. Rep. YALE/DCS/TR-1304, 2004)},

keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{CMZK:CONB,

title = {Geometric diffusions for the analysis of data from sensor networks},

author = {Ronald R Coifman and Mauro Maggioni and Steven W Zucker and Ioannis G Kevrekidis},

year  = {2005},

date = {2005-10-01},

journal = {Curr Opin Neurobiol},

volume = {15},

number = {5},

pages = {576--84},

keywords = {diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{DiffusionPNAS,

title = {Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps},

author = {Ronald R Coifman and Stephane Lafon and Ann B Lee and Mauro Maggioni and B Nadler and Frederick Warner and Steven W Zucker},

year  = {2005},

date = {2005-01-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {102},

number = {21},

pages = {7426-7431},

keywords = {diffusion geometry, Machine learning, Manifold Learning, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{DiffusionPNAS2,

title = {Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods},

author = {Ronald R Coifman and S Lafon and A B Lee and Mauro Maggioni and B Nadler and Frederick Warner and Steven W Zucker},

year  = {2005},

date = {2005-01-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {102},

number = {21},

pages = {7432--7438},

keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},

pubstate = {published},

tppubtype = {article}

}

@article{AuditoryDisplay,

title = {Auditory display of hyperspectral colon tissue images using vocal synthesis models},

author = {Ryan J Cassidy and Jim Berger and Mauro Maggioni and Ronald R Coifman},

year  = {2004},

date = {2004-01-01},

journal = {Proc. 2004 Intern. Con. Auditory Display},

keywords = {Clustering, diffusion geometry, hyperspectral images, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{MMIEEEPath,

title = {Algorithms from Signal and Data Processing Applied to Hyperspectral Analysis: Application to Discriminating Normal and Malignant Microarray Colon Tissue Sections},

author = {Mauro Maggioni and F J Warner and Gus L Davis and Ronald R Coifman and Frank B Geshwind and Andreas C Coppi and R A DeVerse},

year  = {2004},

date = {2004-01-01},

journal = {submitted},

keywords = {Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}