Data Learning on Manifolds and Future Challenges

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Super-resolution and interpolation of the Euclid PSF

Report on applying dictionary learning on manifolds developed in DEDALE in combination with interpolation methods which are used extensively by the UCL group (e.g neural networks and Gaussian processes) to build a model for the Euclid PSF.

Optimization for manifold-valued signal restoration.

Report on optimization methods for signal restoration with manifold-valued representations: designing and implementing algorithms to solve linear inverse problems with non-linear signal representations; The case of non-linear and potentially non-convex problems will be discussed.

Adaptive transforms for manifold-valued data.

Report on the development of adapted multiscale transforms. The final learned dictionary is restricted to a class of dictionaries generated from a structured dictionary such as shearlet. Existence of fast transform/reconstruction will be discussed.

Non-linear learning on complex imaging data.

Report on Non-linear learning on complex imaging data: learning representations for data lying on unknown low-dimensional manifolds. The use of deep learning architectures like stacked sparse autoencoders will be particularly studied in this task.

Large-scale learning schemes.

Evaluation of cutting edge distributed processing platforms, such as GraphLab, Mahout and MLI, for benchmarking large-scale test sets for machine learning. Real time parallel processing considerations will be actively taken under account into this task.

Optimizations for non-linear learning.

Report on building upon proximal methods and problem splitting techniques to design highly-parallelizable sparse solvers (e.g. sparse/low-rank multivariate signal decompositions.)

Learning-based photometric and spectroscopic redshift estimation

Report on the development of a dictionary learning based method for spectroscopic and photometric redshift estimation of Euclid data.

Dictionary learning for multivariate/multispectral data.

Report on Dictionary Learning on multi-valued data. The case of multi-channel polarized data on the sphere will be considered.

Linear inverse problems with sparsity constraints.

Report on the development of dedicated solvers for the recovery of multivariate signals with adapted sparse priors, either in fixed representations from Task 2.1 or learnt representations from task 2.2. Convergence and Computation time will be discussed.

Numerical toolbox and benchmarking platform.

Development of a numerical toolbox and benchmark test set. The code will be in Python and/orC++. The algorithms related to tasks 3.1 and 3.2 will be included in the toolbox, and benchmark data will also be joined to the toolbox for test and evaluation.

Toolbox and benchmarking platform for large scale learning.

Toolbox for parallel linear and non-linear sparsity based learning architectures.

Evaluation/validation of the mass mapping algorithms

Report on using dictionary methods for 2D and 3D mass mapping from weak lensing data. Data available in the framework of the Open Research Data Pilot concept.

Project Website & Factsheet

Realization of web site, contains informations about the project (publications, technical notes, etc).

Publicaciones

Cosmic microwave background reconstruction from WMAP and Planck PR2 data

Autores: J. Bobin, F. Sureau, J.-L. Starck
Publicado en: Astronomy & Astrophysics, Edición 591, 2016, Página(s) A50, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201527822

Sparsity and inverse problems in astrophysics

Autores: Jean-Luc Starck
Publicado en: Journal of Physics: Conference Series, Edición 699, 2016, Página(s) 012010, ISSN 1742-6588
Editor: Institute of Physics
DOI: 10.1088/1742-6596/699/1/012010

High resolution weak lensing mass mapping combining shear and flexion

Autores: F. Lanusse, J.-L. Starck, A. Leonard, S. Pires
Publicado en: Astronomy & Astrophysics, Edición 591, 2016, Página(s) A2, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201628278

Point Spread Function Field Learning Based on Optimal Transport Distances

Autores: F. Ngolé, J.-L. Starck
Publicado en: SIAM Journal on Imaging Sciences, Edición 10/3, 2017, Página(s) 1549-1578, ISSN 1936-4954
Editor: Society for Industrial and Applied Mathematics
DOI: 10.1137/16M1093677

α -Molecules

Autores: Philipp Grohs, Sandra Keiper, Gitta Kutyniok, Martin Schäfer
Publicado en: Applied and Computational Harmonic Analysis, Edición 41/1, 2016, Página(s) 297-336, ISSN 1063-5203
Editor: Academic Press
DOI: 10.1016/j.acha.2015.10.009

Sparse Reconstruction of the Merging A520 Cluster System

Autores: Austin Peel, François Lanusse, Jean-Luc Starck
Publicado en: The Astrophysical Journal, Edición 847/1, 2017, Página(s) 23, ISSN 1538-4357
Editor: IOPScience
DOI: 10.3847/1538-4357/aa850d

Space variant deconvolution of galaxy survey images

Autores: S. Farrens, F. M. Ngolè Mboula, J.-L. Starck
Publicado en: Astronomy & Astrophysics, Edición 601, 2017, Página(s) A66, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201629709

Singular spectrum-based matrix completion for time series recovery and prediction

Autores: Grigorios Tsagkatakis, Baltasar Beferull-Lozano, Panagiotis Tsakalides
Publicado en: EURASIP Journal on Advances in Signal Processing, Edición 2016/1, 2016, ISSN 1687-6180
Editor: springer
DOI: 10.1186/s13634-016-0360-0

Land Classification Using Remotely Sensed Data: Going Multilabel

Autores: Konstantinos Karalas, Grigorios Tsagkatakis, Michael Zervakis, Panagiotis Tsakalides
Publicado en: IEEE Transactions on Geoscience and Remote Sensing, Edición 54/6, 2016, Página(s) 3548-3563, ISSN 0196-2892
Editor: Institute of Electrical and Electronics Engineers
DOI: 10.1109/TGRS.2016.2520203

Matrix and Tensor Completion on a Human Activity Recognition Framework

Autores: Sofia Savvaki, Grigorios Tsagkatakis, Athanasia Panousopoulou, Panagiotis Tsakalides
Publicado en: IEEE Journal of Biomedical and Health Informatics, Edición 21/6, 2017, Página(s) 1554-1561, ISSN 2168-2194
Editor: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/JBHI.2017.2716112

Compressed sensing for finite-valued signals

Autores: Sandra Keiper, Gitta Kutyniok, Dae Gwan Lee, Götz E. Pfander
Publicado en: Linear Algebra and its Applications, Edición 532, 2017, Página(s) 570-613, ISSN 0024-3795
Editor: Elsevier BV
DOI: 10.1016/j.laa.2017.07.006

Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised Nonlinear Dictionary Learning

Autores: Morgan A. Schmitz, Matthieu Heitz, Nicolas Bonneel, Fred Ngolè, David Coeurjolly, Marco Cuturi, Gabriel Peyré, Jean-Luc Starck
Publicado en: SIAM Journal on Imaging Sciences, Edición 11/1, 2018, Página(s) 643-678, ISSN 1936-4954
Editor: Society for Industrial and Applied Mathematics
DOI: 10.1137/17M1140431

A Haar wavelet-based perceptual similarity index for image quality assessment

Autores: Rafael Reisenhofer, Sebastian Bosse, Gitta Kutyniok, Thomas Wiegand
Publicado en: Signal Processing: Image Communication, Edición 61, 2018, Página(s) 33-43, ISSN 0923-5965
Editor: Elsevier BV
DOI: 10.1016/j.image.2017.11.001

Unsupervised feature-learning for galaxy SEDs with denoising autoencoders

Autores: J. Frontera-Pons, F. Sureau, J. Bobin, E. Le Floc’h
Publicado en: Astronomy & Astrophysics, Edición 603, 2017, Página(s) A60, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201630240

Optimal approximation of piecewise smooth functions using deep ReLU neural networks

Autores: Philipp Petersen, Felix Voigtlaender
Publicado en: Neural Networks, Edición 108, 2018, Página(s) 296-330, ISSN 0893-6080
Editor: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2018.08.019

Constraint matrix factorization for space variant PSFs field restoration

Autores: F Ngolè, J-L Starck, K Okumura, J Amiaux, P Hudelot
Publicado en: Inverse Problems, Edición 32/12, 2016, Página(s) 124001, ISSN 0266-5611
Editor: Institute of Physics Publishing
DOI: 10.1088/0266-5611/32/12/124001

Breaking degeneracies in modified gravity with higher (than 2nd) order weak-lensing statistics

Autores: Austin Peel, Valeria Pettorino, Carlo Giocoli, Jean-Luc Starck, Marco Baldi
Publicado en: Astronomy & Astrophysics, Edición 619, 2018, Página(s) A38, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201833481

Cosmological constraints with weak-lensing peak counts and second-order statistics in a large-field survey

Autores: Austin Peel, Chieh-An Lin, François Lanusse, Adrienne Leonard, Jean-Luc Starck, Martin Kilbinger
Publicado en: Astronomy & Astrophysics, Edición 599, 2017, Página(s) A79, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201629928

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

Autores: J D Rivera, B Moraes, A I Merson, S Jouvel, F B Abdalla, M C B Abdalla
Publicado en: Monthly Notices of the Royal Astronomical Society, Edición 477/4, 2018, Página(s) 4330-4347, ISSN 0035-8711
Editor: Blackwell Publishing Inc.
DOI: 10.1093/mnras/sty880

Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: application to DES SV

Autores: N Jeffrey, F B Abdalla, O Lahav, F Lanusse, J-L Starck, A Leonard, D Kirk, C Chang, E Baxter, T Kacprzak, S Seitz, V Vikram, L Whiteway, T M C Abbott, S Allam, S Avila, E Bertin, D Brooks, A Carnero Rosell, M Carrasco Kind, J Carretero, F J Castander, M Crocce, C E Cunha, C B D’Andrea, L N da Costa, C Davis, J De Vicente, S Desai, P Doel, T F Eifler, A E Evrard, B Flaugher, P Fosalba, J Frie
Publicado en: Monthly Notices of the Royal Astronomical Society, Edición 479/3, 2018, Página(s) 2871-2888, ISSN 0035-8711
Editor: Blackwell Publishing Inc.
DOI: 10.1093/mnras/sty1252

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

Autores: R. Joseph, F. Courbin, J.-L. Starck
Publicado en: Astronomy & Astrophysics, Edición 589, 2016, Página(s) A2, ISSN 0004-6361
Editor: Springer Verlag
DOI: 10.1051/0004-6361/201527923

A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations

Autores: Genzel, Martin; Kutyniok, Gitta
Publicado en: Edición 25, 2016
Editor: ArXiv

Optimal Approximation with Sparsely Connected Deep Neural Networks

Autores: Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
Publicado en: 2017
Editor: ArXiv

Robust 1-Bit Compressed Sensing via Hinge Loss Minimization

Autores: Martin Genzel, Alexander Stollenwerk
Publicado en: 2018
Editor: arXiv

Recovering Structured Data From Superimposed Non-Linear Measurements

Autores: Martin Genzel, Peter Jung
Publicado en: 2017
Editor: ArXiv

Compressed Sensing for Analog Signals

Autores: Bernard G. Bodmann, Axel Flinth, Gitta Kutyniok
Publicado en: 2018
Editor: arXiv

Anisotropic Multiscale Systems on Bounded Domains

Autores: Grohs, Philipp; Kutyniok, Gitta; Ma, Jackie; Petersen, Philipp; Raslan, Mones
Publicado en: Edición 33, 2015
Editor: ArXiv

ℓ1-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed?

Autores: Martin Genzel, Gitta Kutyniok, Maximilian März
Publicado en: 2017
Editor: ArXiv

The Mismatch Principle: Statistical Learning Under Large Model Uncertainties

Autores: Martin Genzel, Gitta Kutyniok
Publicado en: 2018
Editor: arXiv

Structured, compactly supported Banach frame decompositions of decomposition spaces

Autores: Voigtlaender, Felix
Publicado en: Edición 21, 2016
Editor: ArXiv

Learning sparse representations on the sphere

Autores: Florent Sureau, Felix Voigtlaender, Malte Wust, Jean-Luc Starck, Gitta Kutyniok
Publicado en: 2018
Editor: arXiv

A Distributed Learning Architecture for Scientific Imaging Problems

Autores: A. Panousopoulou, S. Farrens, K. Fotiadou, A. Woiselle, G. Tsagkatakis, , J.-L. Starck, P. Tsakalides
Publicado en: 2018
Editor: arXiv

Parameter inference and model comparison using theoretical predictions from noisy simulations

Autores: Niall Jeffrey, Filipe B. Abdalla
Publicado en: 2018
Editor: arXiv

Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data

Autores: Radamanthys Stivaktakis, Grigorios Tsagkatakis, Bruno Moraes, Filipe Abdalla, Jean-Luc Starck, Panagiotis Tsakalides
Publicado en: 2018
Editor: arXiv

ZXCorr: Cosmological Measurements from Angular Power Spectra Analysis of BOSS DR12 Tomography

Autores: Arthur Loureiro, Bruno Moraes, Filipe B. Abdalla, Andrei Cuceu, Michael McLeod, Lorne Whiteway, Sreekumar T. Balan, Aurélien Benoit-Lévy, Ofer Lahav, Marc Manera, Richard Rollins, Henrique S. Xavier
Publicado en: 2018
Editor: arXiv

Analysis vs. synthesis sparsity for α-shearlets

Autores: Felix Voigtlaender, Anne Pein
Publicado en: 2017
Editor: arXiv

A distributed learning architecture for big imaging problems in astrophysics

Autores: Athanasia Panousopoulou, Sammuel Farrens, Yannis Mastorakis, Jean-Luck Starck, Panagiotis Tsakailides
Publicado en: 2017 25th European Signal Processing Conference (EUSIPCO), 2017, Página(s) 1440-1444, ISBN 978-0-9928626-7-1
Editor: IEEE
DOI: 10.23919/eusipco.2017.8081447

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