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pro vyhledávání: '"Hautecoeur, Cécile"'
Nonnegative Matrix Factorization (NMF) models are widely used to recover linearly mixed nonnegative data. When the data is made of samplings of continuous signals, the factors in NMF can be constrained to be samples of nonnegative rational functions,
Externí odkaz:
http://arxiv.org/abs/2209.12579
Autor:
Hautecoeur, Cécile, Glineur, François
Publikováno v:
In Neurocomputing 27 November 2020 416:256-265
Akademický článek
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Autor:
Hautecoeur, Cécile
Nonnegative Matrix Factorization (NMF) is a popular data analysis tool for nonnegative data, able to extract meaningful features from a dataset, compress it and filter its noise. To do so, this method factorizes an input matrix Y as the product of tw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1493::7de6016c1be2d6505bd23b770f1f9291
https://hdl.handle.net/2078.1/266123
https://hdl.handle.net/2078.1/266123
Autor:
Hautecoeur, Cécile, Glineur, François
La Factorisation Matricielle Nonnégative (NMF) est une méthode commune pour analyser des données matricielles nonnégatives. Pour améliorer la qualité de la factorisation, il peut être intéressant d’imposer une certaine structure aux facteur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1493::84dee030eefbf2ad283acaa58d9d6ff0
https://hdl.handle.net/2078.1/265363
https://hdl.handle.net/2078.1/265363
Autor:
Hautecoeur, Cécile, Glineur, François, De Lathauwer, Lieven, 2021 29th European Signal Processing Conference (EUSIPCO)
We present an extension of the widely used Hierarchical Alternating Least Squares (HALS) algorithm to solve Nonnegative Matrix Factorization (NMF) problems using rational functions, in order to unmix discretization of continuous signals. We observe t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::007d790a932b70af212cca3b1706f4a1
https://hdl.handle.net/2078.1/254704
https://hdl.handle.net/2078.1/254704
Autor:
Hautecoeur, Cécile, Glineur, François, ESANN 2020,28th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning
When performing image completion, it is common to assume that images are smooth and low-rank, when viewed as matrices of pixel intensities. In this work, we use nonnegative matrix factorization to suc- cessively refine the image by representing alter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1493::09d8b512477fa042fa88f27dfda14139
https://hdl.handle.net/2078.1/229855
https://hdl.handle.net/2078.1/229855
Autor:
Hautecoeur, Cécile, François Glineur, International Workshop on Machine Learning for Signal Processing
Publikováno v:
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), p. 1-6 (2019)
MLSP
MLSP
Nonnegative Matrix Factorization is a data analysis tool that aims at representing a set of input data vectors as nonnegative linear combinations of a few nonnegative basis vectors. When dealing with continuous input signals, smoothness and accuracy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1478fdc8d1d339d079c459cf36e9e950
https://hdl.handle.net/2078.1/224247
https://hdl.handle.net/2078.1/224247
Autor:
Hautecoeur, Cécile, Glineur, François, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Nonnegative matrix factorization (NMF) is a widely used tool in data analysis due to its ability to extract significant features from data vectors. Among algorithms developed to solve NMF, hierarchical alternating least squares (HALS) is often used t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1493::7947e025d3aebac190bf5e17c407d677
https://hdl.handle.net/2078.1/215580
https://hdl.handle.net/2078.1/215580