2022
Zhou, Jin; Maggioni, Mauro
Learning Multiscale Approximations of Functions between Manifolds PhD Thesis
2022.
Abstract | Links | BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, statistics, supervised learning
@phdthesis{nokey,
title = {Learning Multiscale Approximations of Functions between Manifolds},
author = {Jin Zhou and Mauro Maggioni},
url = {https://jscholarship.library.jhu.edu/items/3a61646e-3c03-47a4-9768-180cf67e5fc4/full},
year = {2022},
date = {2022-07-18},
urldate = {2022-07-18},
abstract = {In many machine learning applications, data sets are in a high dimensional space but have a low-dimensional structure. The intrinsic dimension of the structure is often much smaller than the ambient dimension. This has given rise to the studies on manifold learning, when the low-dimensional structure is a manifold, and dictionary learning, when the low-dimensional structure is a set of sparse linear combinations of vectors from a finite dictionary. However, there has been very limited research for transformations between two high dimensional data sets. These transformations can be hard and expensive to store and compute. Furthermore, the existing algorithms are limited to be applied due to the high dimensionality of the two data sets. This thesis considers the problem of estimating a function between two high dimensional data sets. Both the domain and the range are supported on low-dimensional manifolds, given random samples in the domain and corresponding samples in the range perturbed by bounded noise. Geometric Multi-Resolution Analysis (GMRA) constructs low-dimensional geometric multiscale approximations of the data set lying on or near a manifold. We estimate these two unknown manifolds using GMRA and approximate the functions locally by multiscale linear maps. We obtain the optimal learning rate up to a log factor, depending on the intrinsic dimension of data, and circumvent the curse of dimensionality in the domain and the range.},
keywords = {geometric wavelets, Machine learning, Manifold Learning, statistics, supervised learning},
pubstate = {published},
tppubtype = {phdthesis}
}
2019
Liao, Wenjing; Maggioni, Mauro
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data Journal Article
In: Journal of machine learning Research, vol. 20, no. 98, pp. 1-63, 2019.
Links | BibTeX | Tags: geometric wavelets, Machine learning, multiscale analysis, statistics
@article{LiaoMaggioni:GMRA,
title = {Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data},
author = {Wenjing Liao and Mauro Maggioni},
url = {http://jmlr.org/papers/v20/17-252.html},
year = {2019},
date = {2019-01-01},
journal = {Journal of machine learning Research},
volume = {20},
number = {98},
pages = {1-63},
keywords = {geometric wavelets, Machine learning, multiscale analysis, statistics},
pubstate = {published},
tppubtype = {article}
}
2017
Little, Anna V; Maggioni, Mauro; Rosasco, Lorenzo
Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature Journal Article
In: Applied and Computational Harmonic Analysis, vol. 43, no. 3, pp. 504 – 567, 2017, (Submitted: 2012, MIT-CSAIL-TR-2012-029/CBCL-310).
BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics
@article{LMR:MGM1,
title = {Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature},
author = {Anna V Little and Mauro Maggioni and Lorenzo Rosasco},
year = {2017},
date = {2017-01-01},
journal = {Applied and Computational Harmonic Analysis},
volume = {43},
number = {3},
pages = {504 - 567},
note = {Submitted: 2012, MIT-CSAIL-TR-2012-029/CBCL-310},
keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics},
pubstate = {published},
tppubtype = {article}
}
Maggioni, Mauro
Geometric Measure Estimation Journal Article
In: in preparation, 2017.
BibTeX | Tags: geometric wavelets, statistics
@article{CIM:geometricdensityestimation,
title = {Geometric Measure Estimation},
author = {Mauro Maggioni},
year = {2017},
date = {2017-01-01},
journal = {in preparation},
keywords = {geometric wavelets, statistics},
pubstate = {published},
tppubtype = {article}
}
2013
Maggioni, Mauro
Geometric Estimation of Probability Measures in High Dimensions Proceedings Article
In: IEEE Asilomar Conference on Signals, Systems and Computers, 2013.
BibTeX | Tags: geometric wavelets, statistics
@inproceedings{MM_GeometricEstimationAsilomar,
title = {Geometric Estimation of Probability Measures in High Dimensions},
author = {Mauro Maggioni},
year = {2013},
date = {2013-01-01},
booktitle = {IEEE Asilomar Conference on Signals, Systems and Computers},
keywords = {geometric wavelets, statistics},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
Bouvrie, Jake; Maggioni, Mauro
Geometric Multiscale Reduction for Autonomous and Controlled Nonlinear Systems Proceedings Article
In: IEEE Conference on Decision and Control (CDC), 2012.
BibTeX | Tags: control theory, geometric wavelets, Machine learning, multiscale analysis
@inproceedings{BM_GMReductionControlledSystems,
title = {Geometric Multiscale Reduction for Autonomous and Controlled Nonlinear Systems},
author = {Jake Bouvrie and Mauro Maggioni},
year = {2012},
date = {2012-01-01},
booktitle = {IEEE Conference on Decision and Control (CDC)},
keywords = {control theory, geometric wavelets, Machine learning, multiscale analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Allard, William K; Chen, Guangliang; Maggioni, Mauro
Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis Journal Article
In: Applied and Computational Harmonic Analysis, vol. 32, no. 3, pp. 435–462, 2012.
BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics
@article{CM:MGM2,
title = {Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis},
author = {William K Allard and Guangliang Chen and Mauro Maggioni},
year = {2012},
date = {2012-01-01},
journal = {Applied and Computational Harmonic Analysis},
volume = {32},
number = {3},
pages = {435--462},
keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, statistics},
pubstate = {published},
tppubtype = {article}
}
2011
Chen, Guangliang; Little, Anna V; Maggioni, Mauro
Multi-Resolution Geometric Analysis for data in high dimensions Journal Article
In: Proc. FFT 2011, 2011.
BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis
@article{FFT2011,
title = {Multi-Resolution Geometric Analysis for data in high dimensions},
author = {Guangliang Chen and Anna V Little and Mauro Maggioni},
year = {2011},
date = {2011-08-01},
journal = {Proc. FFT 2011},
keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis},
pubstate = {published},
tppubtype = {article}
}
Maggioni, Mauro
Multiscale Geometric Dictionaries for Point-Cloud Data, Presented at SPARS 11, http://www.math.duke.edu/~mauro/research.html#Talks Journal Article
In: 2011, (Presented at SPARS 11).
Links | BibTeX | Tags: dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis
@article{MM:Spars11talk,
title = {Multiscale Geometric Dictionaries for Point-Cloud Data, Presented at SPARS 11, http://www.math.duke.edu/~mauro/research.html#Talks},
author = {Mauro Maggioni},
url = {http://www.math.duke.edu/~mauro/research.html#Talks},
year = {2011},
date = {2011-06-01},
note = {Presented at SPARS 11},
keywords = {dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis},
pubstate = {published},
tppubtype = {article}
}
Chen, Guangliang; Maggioni, Mauro
Multiscale Geometric Dictionaries for Point-Cloud Data Proceedings Article
In: Proc. SampTA, 2011.
BibTeX | Tags: dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis
@inproceedings{CM:SamptaGW,
title = {Multiscale Geometric Dictionaries for Point-Cloud Data},
author = {Guangliang Chen and Mauro Maggioni},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. SampTA},
keywords = {dictionary learning, geometric wavelets, Machine learning, Manifold Learning, multiscale analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
2010
Monson, Eric; Chen, Guangliang; Brady, Rachel; Maggioni, Mauro
Data representation and exploration with Geometric Wavelets Proceedings Article
In: Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium, pp. 243–244, 2010.
Links | BibTeX | Tags: geometric wavelets, visualization
@inproceedings{MM:Vast2010,
title = {Data representation and exploration with Geometric Wavelets},
author = {Eric Monson and Guangliang Chen and Rachel Brady and Mauro Maggioni},
doi = {10.1109/VAST.2010.5653822},
year = {2010},
date = {2010-12-01},
booktitle = {Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium},
pages = {243--244},
keywords = {geometric wavelets, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Monson, Eric; Chen, Guangliang; Brady, Rachel; Maggioni, Mauro
Data Representation and Exploration with Geometric Wavelets Proceedings Article
In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST), 2010.
BibTeX | Tags: geometric wavelets, visualization
@inproceedings{Monson:VAST2010,
title = {Data Representation and Exploration with Geometric Wavelets},
author = {Eric Monson and Guangliang Chen and Rachel Brady and Mauro Maggioni},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST)},
keywords = {geometric wavelets, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Chen, Guangliang; Maggioni, Mauro
Multiscale Geometric Wavelets for the Analysis of Point Clouds Journal Article
In: Proc. CISS 2010, 2010.
BibTeX | Tags: geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, spectral graph theory
@article{CM:geometricwaveletsciss,
title = {Multiscale Geometric Wavelets for the Analysis of Point Clouds},
author = {Guangliang Chen and Mauro Maggioni},
year = {2010},
date = {2010-01-01},
journal = {Proc. CISS 2010},
keywords = {geometric wavelets, Machine learning, Manifold Learning, multiscale analysis, spectral graph theory},
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.