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FACTORY SIGNED

New paradigms for latent factor estimation

Total Cost €

0

EC-Contrib. €

0

Partnership

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 FACTORY project word cloud

Explore the words cloud of the FACTORY project. It provides you a very rough idea of what is the project "FACTORY" about.

factorisation    entails    rank    break    retrieval    inducing    completion    discovery    pays    matrix    paradigm    diverse    computed    recommendation    yields    constraints    unmixing    world    reconsidered    societal    sensing    soft    recurring    settings    estimation    object    latent    learning    rarely    limit    describes    paradigms    models    dominant    semantic    made    complies    music    thirdly    statistical    transforms    mining    ubiquitous    enhanced    gaussian    enhancement    multimodal    preprocess    usually    optimal    assumption    lfe    clustering    appears    first    observation    patterns    frontiers    deterministic    speech    data    audio    push    setting    remastering    earth    signal    single    methodology    difficult    cosmic    names    domain    performance    proportions    transform    form    collection    loosely    dimensionality    off    dictionary    nature    coding    marginal    too    explore    shown    remote    approximation    approximate    practical    shelf    samples    raw    columns    geometry    learnt   

Project "FACTORY" data sheet

The following table provides information about the project.

Coordinator
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS 

Organization address
address: RUE MICHEL ANGE 3
city: PARIS
postcode: 75794
website: www.cnrs.fr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Project website http://projectfactory.irit.fr/
 Total cost 1˙931˙776 €
 EC max contribution 1˙931˙776 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-CoG
 Funding Scheme ERC-COG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR (PARIS) coordinator 1˙931˙776.00

Map

 Project objective

Data is often available in matrix form, in which columns are samples, and processing of such data often entails finding an approximate factorisation of the matrix in two factors. The first factor yields recurring patterns characteristic of the data. The second factor describes in which proportions each data sample is made of these patterns. Latent factor estimation (LFE) is the problem of finding such a factorisation, usually under given constraints. LFE appears under other domain-specific names such as dictionary learning, low-rank approximation, factor analysis or latent semantic analysis. It is used for tasks such as dimensionality reduction, unmixing, soft clustering, coding or matrix completion in very diverse fields.

In this project, I propose to explore three new paradigms that push the frontiers of traditional LFE. First, I want to break beyond the ubiquitous Gaussian assumption, a practical choice that too rarely complies with the nature and geometry of the data. Estimation in non-Gaussian models is more difficult, but recent work in audio and text processing has shown that it pays off in practice. Second, in traditional settings the data matrix is often a collection of features computed from raw data. These features are computed with generic off-the-shelf transforms that loosely preprocess the data, setting a limit to performance. I propose a new paradigm in which an optimal low-rank inducing transform is learnt together with the factors in a single step. Thirdly, I show that the dominant deterministic approach to LFE should be reconsidered and I propose a novel statistical estimation paradigm, based on the marginal likelihood, with enhanced capabilities. The new methodology is applied to real-world problems with societal impact in audio signal processing (speech enhancement, music remastering), remote sensing (Earth observation, cosmic object discovery) and data mining (multimodal information retrieval, user recommendation).

 Publications

year authors and title journal last update
List of publications.
2016 R. Flamary, C. Févotte, N. Courty, and V. Emiya
Optimal spectral transportation with application to music transcription
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems (NIPS) 2019-06-18
2018 D. Fagot, H. Wendt, and C. Févotte
Nonnegative matrix factorization with transform learning
published pages: , ISSN: , DOI:
Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019-06-18
2018 A. Ozerov, C. Févotte, and E. Vincent
An introduction to multichannel NMF for audio source separation
published pages: , ISSN: , DOI:
Audio Source Separation 2019-06-18
2018 C. Févotte, E. Vincent, and A. Ozerov
Single-channel audio source separation with NMF: divergences, constraints and algorithms
published pages: , ISSN: , DOI:
Audio Source Separation 2019-06-18
2018 C. Févotte, P. Smaragdis, N. Mohammadiha, and G. Mysore
Temporal extensions of nonnegative matrix factorization
published pages: , ISSN: , DOI:
Audio Source Separation and Speech Enhancement 2019-06-18
2018 L. Filstroff, A. Lumbreras, and C. Févotte
Closed-form marginal likelihood in Gamma-Poisson matrix factorization
published pages: , ISSN: , DOI:
Proc. International Conference on Machine Learning (ICML) 2019-05-27
2018 O. Gouvert, T. Oberlin, and C. Févotte
Matrix co-factorization for cold-start recommendation
published pages: , ISSN: , DOI:
Proc. International Society for Music Information Retrieval Conference (ISMIR) 2019-05-27
2019 Y. C. Cavalcanti, T. Oberlin, N. Dobigeon, C. Févotte, S. Stute, and C. Tauber
Factor analysis of dynamic PET images: beyond Gaussian noise
published pages: , ISSN: 0278-0062, DOI:
IEEE Transactions on Medical Imaging 2019-05-15
2019 R. Xia, V. Y. F. Tan, L. Filstroff and C. Févotte
A ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments
published pages: , ISSN: , DOI:
arXiv 2019-05-15
2018 H. Wendt, D. Fagot, and C. Févotte
Jacobi algorithm for nonnegative matrix factorization with transform learning
published pages: , ISSN: , DOI:
Proc. European Signal Processing Conference (EUSIPCO) 2019-04-18
2018 A. Lumbreras, L. Filstroff, and C. Févotte
Bayesian mean-parameterized nonnegative binary matrix factorization
published pages: , ISSN: , DOI:
arXiv 2019-04-18
2019 P. Ablin, D. Fagot, H. Wendt, A. Gramfort, and C. Févotte
A quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
published pages: , ISSN: , DOI:
Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019-04-18
2019 Y. C. Cavalcanti, T. Oberlin, N. Dobigeon, C. Févotte, S. Stute, and C. Tauber
Unmixing dynamic PET images: Combining spatial heterogeneity and non-gaussian noise
published pages: , ISSN: , DOI:
Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019-04-18
2018 O. Gouvert, T. Oberlin, and C. Févotte
Negative binomial matrix factorization for recommender systems
published pages: , ISSN: , DOI:
arXiv 2019-04-18
2019 D. Fagot, H. Wendt, C. Févotte, and P. Smaragdis
Majorization-minimization algorithms for convolutive NMF with the beta-divergence
published pages: , ISSN: , DOI:
Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019-04-18
2018 C. Févotte and M. Kowalski
Estimation with low-rank time-frequency synthesis models
published pages: , ISSN: 1053-587X, DOI:
IEEE Transactions on Signal Processing 2019-04-18

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