Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Paris V. Giampouras"'
Publikováno v:
IEEE Transactions on Signal Processing
The so-called block-term decomposition (BTD) tensor model, especially in its rank-$(L_r,L_r,1)$ version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{block
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d64f9e67e8f61c4991b5cd44992d77a8
https://doi.org/10.1109/tsp.2022.3159029
https://doi.org/10.1109/tsp.2022.3159029
A Projected Newton-type Algorithm for Rank - revealing Nonnegative Block - Term Tensor Decomposition
Publikováno v:
2022 30th European Signal Processing Conference (EUSIPCO).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37a74f28eb1842e94b5e92da73156767
https://doi.org/10.23919/eusipco55093.2022.9909799
https://doi.org/10.23919/eusipco55093.2022.9909799
Publikováno v:
2021 29th European Signal Processing Conference (EUSIPCO).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d52b4d0ba9684b38fa8c857f4ee3e60d
https://doi.org/10.23919/eusipco54536.2021.9616222
https://doi.org/10.23919/eusipco54536.2021.9616222
Publikováno v:
IEEE Transactions on Signal Processing. 67:490-503
Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information. In that vein, low-rank representations (LRRs), which s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cf2e3c00ca26dd0ebabcb17b55ecbc5
https://doi.org/10.1109/tsp.2018.2883921
https://doi.org/10.1109/tsp.2018.2883921
Publikováno v:
ICASSP
The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of blocks of rank higher than one, a scenario encountered in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61901b0401ac0935982f40be76e20fca
https://doi.org/10.1109/icassp39728.2021.9415104
https://doi.org/10.1109/icassp39728.2021.9415104
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing
EUSIPCO
EUSIPCO
The so-called block-term decomposition (BTD) tensor model has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of blocks of rank higher than one, a scenario encountered in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a8df59c53a4bf28454c36b70347fb7c
Publikováno v:
IEEE Signal Processing Letters
The letter deals with the problem known as robust principal component analysis (RPCA), that is, the decomposition of a data matrix as the sum of a low-rank matrix component and a sparse matrix component. After expressing the low-rank matrix component
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f340625058b8f8e01671069233d94cd
Autor:
Konstantinos Koutroumbas, Ioannis C. Tsaknakis, Athanasios A. Rontogiannis, Paris V. Giampouras
Publikováno v:
IEEE Signal Processing Letters. 25:1266-1270
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e70e31e1ef1af009bf3aa63d58f277e
https://doi.org/10.1109/lsp.2018.2852490
https://doi.org/10.1109/lsp.2018.2852490
Publikováno v:
ICASSP
Nonnegative matrix factorization (NMF) has attracted considerable attention over the past few years as is met in many modern machine learning applications. NMF presents some inherent challenges when it comes both to its theoretical understanding and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4fc1a7cfc810972d7e28c2b1ff3adf76
https://doi.org/10.1109/icassp.2019.8682910
https://doi.org/10.1109/icassp.2019.8682910
Publikováno v:
ICIP
Nowadays, many modern imaging applications generate large-scale and high-dimensional data. In order to efficiently handle these data, statistical tools amenable to exploiting their intrisic low-dimensional nature are needed. PCA is a ubiquitous metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51e752194b562b6313e03f166c1bb4a5
https://doi.org/10.1109/icip.2018.8451212
https://doi.org/10.1109/icip.2018.8451212