## 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). Links | 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}, 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} } |

## 2016 |

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} } |

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} } |

## 2004 |

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} } |

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. @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} } |

- Lectures at Summer School at Peking University, July 2017.
- PCMI Lectures, Summer 2016: Lecture 1, Lecture 2, Problems/discussion points
- Google Scholar
- Papers on the ArXiv
- Papers on MathsciNet
- Tutorials on diffusion geometry and multiscale analysis on graphs at the MRA Internet Program at IPAM: Part I and Part II.
- Diffusion Geometries, Diffusion Wavelets and Harmonic Analysis of large data sets, IPAM, Multiscale Geometric Analysis Program, Fall 2004.
- Diffusion Geometries, global and multiscale, IPAM, 2005.