Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Nicolas Gillis"'
Publikováno v:
SA Journal of Industrial Psychology, Vol 46, Iss 0, Pp e1-e13 (2020)
Orientation: Considering the negative and positive consequences of burnout and work engagement, respectively (i.e. concepts used in reference to well-being), for both workers and organisations, it is crucial to identify their antecedents in order to
Externí odkaz:
https://doaj.org/article/bd83cd2a4bd749aa964b1802837af9f6
Autor:
Nicolas Gillis, Róbert Rajkó
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 44:27-52
Given a nonnegative matrix factorization, $R$, and a factorization rank, $r$, Exact nonnegative matrix factorization (Exact NMF) decomposes $R$ as the product of two nonnegative matrices, $C$ and $S$ with $r$ columns, such as $R = CS^\top$. A central
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93a44559bb15c849bb3bb62130910ad4
https://doi.org/10.1137/22m1507553
https://doi.org/10.1137/22m1507553
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
Publikováno v:
Machine Learning. 111:4453-4495
Nonnegative least squares problems with multiple right-hand sides (MNNLS) arise in models that rely on additive linear combinations. In particular, they are at the core of most nonnegative matrix factorization algorithms and have many applications. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd951d7def6706f8d4b2513b5461723f
https://doi.org/10.1007/s10994-022-06260-2
https://doi.org/10.1007/s10994-022-06260-2
Publikováno v:
Computational Optimization and Applications. 83:247-285
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b0ab7e2e10e385054a7803cdee495db
https://doi.org/10.1007/s10589-022-00394-8
https://doi.org/10.1007/s10589-022-00394-8
Autor:
Christos Kolomvakis, Nicolas Gillis
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6b61070a16d04afd8bb0765835b0d14
https://doi.org/10.1109/icassp49357.2023.10096739
https://doi.org/10.1109/icassp49357.2023.10096739
Publikováno v:
SIAM Journal on Mathematics of Data Science. 4:1-25
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::582398796728bd2b82f19e73e9e6a280
https://doi.org/10.1137/21m1432661
https://doi.org/10.1137/21m1432661
Autor:
Pierre De Handschutter, Nicolas Gillis
Deep matrix factorizations (deep MFs) are recent unsupervised data mining techniques inspired by constrained low-rank approximations. They aim to extract complex hierarchies of features within high-dimensional datasets. Most of the loss functions pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26d94d65d65d37905834dd3e7b3b90f1
http://arxiv.org/abs/2206.10693
http://arxiv.org/abs/2206.10693
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ee4eb07e2daf2f447f43825ad7632b1
https://doi.org/10.1109/icassp43922.2022.9747124
https://doi.org/10.1109/icassp43922.2022.9747124
Autor:
Maryam Abdolali, Nicolas Gillis
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53a75a7cc7c1970110d74e194fb1852e
https://doi.org/10.1109/icassp43922.2022.9746944
https://doi.org/10.1109/icassp43922.2022.9746944