2019
|
Murphy, James M; Maggioni, Mauro Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion Journal Article IEEE Transactions on Geoscience and Remote Sensing, 57 (3), pp. 1829-1845, 2019, ISSN: 1558-0644. Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral images, imaging, Machine learning, Unsupervised Learning @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}
}
|
Little, Anna V; Maggioni, Mauro; Murphy, James M Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms Journal Article Journ. Mach. Learn. Res., 21 , pp. 1-66, 2019. Links | BibTeX | Tags: Clustering, diffusion geometry, Machine learning, Unsupervised Learning @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}
}
|
Maggioni, Mauro; Murphy, James M Learning by active nonlinear diffusion Journal Article Foundations of Data Science, 1 ("2639-8001-2019-3-271"), pp. 271, 2019, ISSN: A0000-0002. Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @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}
}
|
2018
|
Murphy, James M; Maggioni, Mauro Iterative Active Learning with Diffusion Geometry for Hyperspectral Images Inproceedings Proc. of WHISPERS, 2018. Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral images, imaging, Machine learning @inproceedings{whispers2018,
title = {Iterative Active Learning with Diffusion Geometry for Hyperspectral Images},
author = {James M Murphy and Mauro Maggioni},
url = {https://ieeexplore.ieee.org/abstract/document/8747033},
year = {2018},
date = {2018-01-01},
booktitle = {Proc. of WHISPERS},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral images, imaging, Machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Murphy, James M; Maggioni, Mauro Diffusion geometric methods for fusion of remotely sensed data Inproceedings Velez-Reyes, Miguel; Messinger, David W (Ed.): Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, pp. 137 – 147, International Society for Optics and Photonics SPIE, 2018. Links | BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral images, imaging, Machine learning, Unsupervised Learning @inproceedings{10.1117/12.2305274,
title = {Diffusion geometric methods for fusion of remotely sensed data},
author = {James M Murphy and Mauro Maggioni},
editor = {Miguel Velez-Reyes and David W Messinger},
url = {https://doi.org/10.1117/12.2305274},
doi = {10.1117/12.2305274},
year = {2018},
date = {2018-01-01},
booktitle = {Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV},
volume = {10644},
pages = {137 -- 147},
publisher = {SPIE},
organization = {International Society for Optics and Photonics},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral images, imaging, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2017
|
Crosskey, Miles C; Maggioni, Mauro ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds Journal Article Journal of Multiscale Modeling and Simulation, 15 (1), pp. 110–156, 2017, (arxiv: 1404.0667). BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, statistics, stochastic systems @article{CM:ATLAS,
title = {ATLAS: A geometric approach to learning high-dimensional stochastic systems near manifolds},
author = {Miles C Crosskey and Mauro Maggioni},
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}
}
|
2016
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Wang, Yang; Chen, Guangliang; Maggioni, Mauro High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies 2016. BibTeX | Tags: Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @journal{WCM:HSIandMovies,
title = {High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies},
author = {Yang Wang and Guangliang Chen and Mauro Maggioni},
year = {2016},
date = {2016-01-01},
journal = {IEEE Journal of selected topics in applied Earth observations and remote sensing},
volume = {9},
number = {9},
pages = {4316--4324},
keywords = {Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {journal}
}
|
2015
|
Maggioni, Mauro Wang Y; Chen, Guangliang Enhanced Detection of Chemical Plumes in Hyperspectral Images and Movies through Improved Background Modeling Inproceedings Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015. BibTeX | Tags: Active Learning, Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @inproceedings{WangChenMaggioni:Whispers15,
title = {Enhanced Detection of Chemical Plumes in Hyperspectral Images and Movies through Improved Background Modeling},
author = {Mauro Y. Wang Maggioni and Guangliang Chen},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
keywords = {Active Learning, Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2012
|
Chen, Guangliang; Iwen, Mark A; Chin, Peter S; Maggioni, Mauro A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements Inproceedings Visual Communications and Image Processing (VCIP), 2012 IEEE, pp. 1-6, 2012. BibTeX | Tags: Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @inproceedings{6410789,
title = {A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements},
author = {Guangliang Chen and Mark A Iwen and Peter S Chin and Mauro Maggioni},
year = {2012},
date = {2012-01-01},
booktitle = {Visual Communications and Image Processing (VCIP), 2012 IEEE},
pages = {1-6},
keywords = {Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Chen, Guangliang; Iwen, Mark A; Chin, Peter S; Maggioni, Mauro A Fast Multiscale Framework for Data in High Dimensions: Measure Estimation, Anomaly Detection, and Compressive Measurements Inproceedings Visual Communications and Image Processing (VCIP), 2012 IEEE, pp. 1-6, 2012. Links | BibTeX | Tags: Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @inproceedings{CIMC:vcip2012,
title = {A Fast Multiscale Framework for Data in High Dimensions: Measure Estimation, Anomaly Detection, and Compressive Measurements},
author = {Guangliang Chen and Mark A Iwen and Peter S Chin and Mauro Maggioni},
doi = {10.1109/VCIP.2012.6410789},
year = {2012},
date = {2012-01-01},
booktitle = {Visual Communications and Image Processing (VCIP), 2012 IEEE},
pages = {1-6},
keywords = {Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2011
|
Zheng, W; Rohrdanz, M A; Maggioni, Mauro; Clementi, Cecilia Polymer reversal rate calculated via locally scaled diffusion map Journal Article J. Chem. Phys., (134), pp. 144108, 2011. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, molecular dynamics, stochastic systems @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}
}
|
Rohrdanz, M A; Zheng, W; Maggioni, Mauro; Clementi, Cecilia Determination of reaction coordinates via locally scaled diffusion map Journal Article J. Chem. Phys., (134), pp. 124116, 2011. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, molecular dynamics, stochastic systems @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}
}
|
2010
|
Jones, Peter W; Maggioni, Mauro; Schul, Raanan Universal local manifold parametrizations via heat kernels and eigenfunctions of the Laplacian Journal Article Ann. Acad. Scient. Fen., 35 , pp. 1–44, 2010, (http://arxiv.org/abs/0709.1975). BibTeX | Tags: diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory @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}
}
|
2008
|
Coifman, Ronald R; Maggioni, Mauro Geometry Analysis and Signal Processing on Digital Data, Emergent Structures, and Knowledge Building Miscellaneous SIAM News, 2008. BibTeX | Tags: diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory @misc{CM:SiamNews,
title = {Geometry Analysis and Signal Processing on Digital Data, Emergent Structures, and Knowledge Building},
author = {Ronald R Coifman and Mauro Maggioni},
year = {2008},
date = {2008-11-01},
howpublished = {SIAM News},
keywords = {diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory},
pubstate = {published},
tppubtype = {misc}
}
|
Szlam, Arthur D; Maggioni, Mauro; Coifman, Ronald R Regularization on Graphs with Function-adapted Diffusion Processes Journal Article Jour. Mach. Learn. Res., (9), pp. 1711–1739, 2008, ((YALE/DCS/TR1365, Yale Univ, July 2006)). BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, random walks, semisupervised learning, spectral graph theory @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}
}
|
Maggioni, Mauro; Mhaskar, Hrushikesh Diffusion polynomial frames on metric measure spaces Journal Article ACHA, 3 , pp. 329–353, 2008. BibTeX | Tags: approximation theory, diffusion geometry, heat kernels, Laplacian eigenfunctions, multiscale analysis @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}
}
|
Jones, Peter W; Maggioni, Mauro; Schul, Raanan Manifold parametrizations by eigenfunctions of the Laplacian and heat kernels Journal Article Proc. Nat. Acad. Sci., 105 (6), pp. 1803–1808, 2008. BibTeX | Tags: diffusion geometry, heat kernels, Laplacian eigenfunctions, Manifold Learning, multiscale analysis, random walks, spectral graph theory @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}
}
|
Coifman, Ronald R; Kevrekidis, Ioannis G; Lafon, Stephane; Maggioni, Mauro; Nadler, Boaz Diffusion Maps, reduction coordinates and low dimensional representation of stochastic systems Journal Article SIAM J.M.M.S., 7 (2), pp. 842–864, 2008. BibTeX | Tags: diffusion geometry, dynamical systems, Laplacian eigenfunctions, Machine learning, model reduction, stochastic systems @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}
}
|
2007
|
Coifman, Ronald R; Maggioni, Mauro Multiscale Data Analysis with Diffusion Wavelets Journal Article Proc. SIAM Bioinf. Workshop, Minneapolis, 2007. BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @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}
}
|
Mahadevan, Sridhar; Maggioni, Mauro Proto-value Functions: A Spectral Framework for Solving Markov Decision Processes Journal Article JMLR, 8 , pp. 2169–2231, 2007. BibTeX | Tags: diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory @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}
}
|
2006
|
Coifman, Ronald R; Maggioni, Mauro Diffusion Wavelets Journal Article Appl. Comp. Harm. Anal., 21 (1), pp. 53–94, 2006, ((Tech. Rep. YALE/DCS/TR-1303, Yale Univ., Sep. 2004)). BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @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}
}
|
Bremer, James Jr. C; Coifman, Ronald R; Maggioni, Mauro; Szlam, Arthur D Diffusion Wavelet Packets Journal Article Appl. Comp. Harm. Anal., 21 (1), pp. 95–112, 2006, ((Tech. Rep. YALE/DCS/TR-1304, 2004)). BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @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}
}
|
Coifman, Ronald R; Lafon, Stephane; Maggioni, Mauro; Keller, Y; Szlam, A D; Warner, F J; Zucker, S W Geometries of sensor outputs, inference, and information processing Inproceedings Athale, John Zolper; Eds. Intelligent Integrated Microsystems; Ravindra C A (Ed.): Proc. SPIE, pp. 623209, 2006. BibTeX | Tags: diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, spectral graph theory, stochastic systems @inproceedings{CLMKSWZ:GeometrySensorOutputs,
title = {Geometries of sensor outputs, inference, and information processing},
author = {Ronald R Coifman and Stephane Lafon and Mauro Maggioni and Y Keller and A D Szlam and F J Warner and S W Zucker},
editor = {John Zolper; Eds. C Intelligent Integrated Microsystems; Ravindra A. Athale},
year = {2006},
date = {2006-05-01},
booktitle = {Proc. SPIE},
volume = {6232},
pages = {623209},
keywords = {diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, spectral graph theory, stochastic systems},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Maggioni, Mauro; Mahadevan, Sridhar Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes Inproceedings ICML 2006, pp. 601–608, 2006. BibTeX | Tags: diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory @inproceedings{smmm:FastDirectMDP,
title = {Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes},
author = {Mauro Maggioni and Sridhar Mahadevan},
year = {2006},
date = {2006-01-01},
booktitle = {ICML 2006},
pages = {601--608},
keywords = {diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Mahadevan, Sridhar; Ferguson, Kim; Osentoski, Sarah; Maggioni, Mauro Simultaneous Learning of Representation and Control In Continuous Domains Inproceedings AAAI, AAAI Press, 2006. BibTeX | Tags: diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory @inproceedings{smkfsomm:SimLearningReprControlContinuous,
title = {Simultaneous Learning of Representation and Control In Continuous Domains},
author = {Sridhar Mahadevan and Kim Ferguson and Sarah Osentoski and Mauro Maggioni},
year = {2006},
date = {2006-01-01},
booktitle = {AAAI},
publisher = {AAAI Press},
keywords = {diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2005
|
Coifman, Ronald R; Maggioni, Mauro; Zucker, Steven W; Kevrekidis, Ioannis G Geometric diffusions for the analysis of data from sensor networks Journal Article Curr Opin Neurobiol, 15 (5), pp. 576–84, 2005. BibTeX | Tags: diffusion geometry, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, spectral graph theory, stochastic systems @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}
}
|
Coifman, Ronald R; Lafon, Stephane; Lee, Ann B; Maggioni, Mauro; Nadler, B; Warner, Frederick; Zucker, Steven W Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps Journal Article Proceedings of the National Academy of Sciences of the United States of America, 102 (21), pp. 7426-7431, 2005. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, random walks, spectral graph theory, stochastic systems @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}
}
|
Coifman, Ronald R; Lafon, S; Lee, A B; Maggioni, Mauro; Nadler, B; Warner, Frederick; Zucker, Steven W Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods Journal Article Proceedings of the National Academy of Sciences of the United States of America, 102 (21), pp. 7432–7438, 2005. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @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}
}
|
Mahadevan, Sridhar; Maggioni, Mauro Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions Inproceedings University of Massachusetts, Department of Computer Science Technical Report TR-2005-38; Proc. NIPS 2005, 2005. BibTeX | Tags: diffusion geometry, diffusion wavelets, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory @inproceedings{smmm:ValueFunction,
title = {Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions},
author = {Sridhar Mahadevan and Mauro Maggioni},
year = {2005},
date = {2005-01-01},
booktitle = {University of Massachusetts, Department of Computer Science Technical Report TR-2005-38; Proc. NIPS 2005},
keywords = {diffusion geometry, diffusion wavelets, Laplacian eigenfunctions, Machine learning, Manifold Learning, random walks, reinforcement learning, representation learning, spectral graph theory},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Maggioni, Mauro; Bremer, James Jr. C; Coifman, Ronald R; Szlam, Arthur D Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs Conference 5914 (1), SPIE, San Diego, CA, USA, 2005. Links | BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory @conference{MBCS:BiorthogonalDiffusionWavelets,
title = {Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs},
author = {Mauro Maggioni and James Jr. C Bremer and Ronald R Coifman and Arthur D Szlam},
editor = {Manos Papadakis and Andrew F Laine and Michael A Unser},
url = {http://link.aip.org/link/?PSI/5914/59141M/1},
year = {2005},
date = {2005-01-01},
journal = {Wavelets XI},
volume = {5914},
number = {1},
pages = {59141M},
publisher = {SPIE},
address = {San Diego, CA, USA},
keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory},
pubstate = {published},
tppubtype = {conference}
}
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Szlam, Arthur D; Maggioni, Mauro; Coifman, Ronald R; Bremer, James Jr. C Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions Conference 5914-1 , SPIE, San Diego, CA, USA, 2005. Links | BibTeX | Tags: diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory @conference{MSCB:MultiscaleManifoldMethods,
title = {Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions},
author = {Arthur D Szlam and Mauro Maggioni and Ronald R Coifman and James Jr. C Bremer},
editor = {Manos Papadakis and Andrew F Laine and Michael A Unser},
url = {http://link.aip.org/link/?PSI/5914/59141D/1},
year = {2005},
date = {2005-01-01},
journal = {Wavelets XI},
volume = {5914-1},
pages = {59141D},
publisher = {SPIE},
address = {San Diego, CA, USA},
keywords = {diffusion geometry, diffusion wavelets, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory},
pubstate = {published},
tppubtype = {conference}
}
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2004
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Coifman, Ronald R; Maggioni, Mauro Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms Technical Report Dept. Comp. Sci., Yale University (YALE/DCS/TR-1289), 2004. BibTeX | Tags: diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems @techreport{CMTech,
title = {Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms},
author = {Ronald R Coifman and Mauro Maggioni},
year = {2004},
date = {2004-05-01},
number = {YALE/DCS/TR-1289},
institution = {Dept. Comp. Sci., Yale University},
keywords = {diffusion geometry, Machine learning, Manifold Learning, multiscale analysis, random walks, spectral graph theory, stochastic systems},
pubstate = {published},
tppubtype = {techreport}
}
|
Maggioni, Mauro; Warner, F J; Davis, Gus L; Coifman, Ronald R; Geshwind, Frank B; Coppi, Andreas C; DeVerse, R A Algorithms from Signal and Data Processing Applied to Hyperspectral Analysis: Application to Discriminating Normal and Malignant Microarray Colon Tissue Sections Technical Report Yale University Dept. Comp. Sci., (1311), 2004. BibTeX | Tags: Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @techreport{MMPathTechRep,
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 = {2004-02-01},
number = {1311},
address = {Dept. Comp. Sci.},
institution = {Yale University},
keywords = {Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning},
pubstate = {published},
tppubtype = {techreport}
}
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Cassidy, Ryan J; Berger, Jim; Maggioni, Mauro; Coifman, Ronald R Auditory display of hyperspectral colon tissue images using vocal synthesis models Journal Article Proc. 2004 Intern. Con. Auditory Display, 2004. BibTeX | Tags: Clustering, diffusion geometry, hyperspectral images, Machine learning @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}
}
|
Maggioni, Mauro; Warner, F J; Davis, Gus L; Coifman, Ronald R; Geshwind, Frank B; Coppi, Andreas C; DeVerse, R A Algorithms from Signal and Data Processing Applied to Hyperspectral Analysis: Application to Discriminating Normal and Malignant Microarray Colon Tissue Sections Journal Article submitted, 2004. BibTeX | Tags: Clustering, diffusion geometry, hyperspectral images, Machine learning, Unsupervised Learning @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}
}
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