Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Cécile Hautecoeur"'
Publikováno v:
IEEE Transactions on Signal Processing. 71:1712-1724
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49c510ce7b02921bc8a50cd3aa1b0938
https://doi.org/10.1109/tsp.2023.3260560
https://doi.org/10.1109/tsp.2023.3260560
Autor:
Cécile Hautecoeur, François Glineur
Publikováno v:
Neurocomputing, Vol. 416, p. 256–265 (2020)
Nonnegative matrix factorization is a popular data analysis tool able to extract significant features from nonnegative data. We consider an extension of this problem to handle functional data, using parametrizable nonnegative functions such as polyno
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::476b33d241f55fb7490fce5b51bbff1f
https://hdl.handle.net/2078.1/227889
https://hdl.handle.net/2078.1/227889
Publikováno v:
Development (09501991); Aug2024, Vol. 151 Issue 16, p1-12, 12p
Autor:
Paul Lheureux
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