Zobrazeno 1 - 10
of 353
pro vyhledávání: '"Multilinear subspace learning"'
Autor:
Mohammad M. Salut, David V. Anderson
Publikováno v:
IEEE Access, Vol 10, Pp 69354-69363 (2022)
Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames)
Externí odkaz:
https://doaj.org/article/7fcddcb050414e1494154fbff9e236af
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Applied Intelligence. 51:3534-3547
In this paper, we propose a new multilinear and multiview subspace learning method called Tensor Cross-view Quadratic Discriminant Analysis for face kinship verification in the wild. Most of the existing multilinear subspace learning methods straight
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4c950f3d9da3d264003baade081b20a
https://doi.org/10.1007/s10489-020-02044-0
https://doi.org/10.1007/s10489-020-02044-0
Autor:
Yiu-ming Cheung, Yang Zhou
Publikováno v:
IEEE Transactions on Cybernetics. 50:627-639
Linear discriminant analysis (LDA) is a classical supervised subspace learning technique that has wide applications. However, it is designed for vector only, which cannot exploit the tensor structures and may lead to suboptimal results when dealing w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf5c1ff5928695c1ddad7762d9cca907
https://doi.org/10.1109/tcyb.2018.2870440
https://doi.org/10.1109/tcyb.2018.2870440
Autor:
Christopher S. Johns, Robin Condliffe, Jim M. Wild, Peter Metherall, Haiping Lu, Marcella Cogliano, Jonathan Taylor, Andrew J. Swift, Shuo Zhou, Samer Alabed, Johanna Uthoff, Allan Lawrie, Pankaj Garg, David G. Kiely
Publikováno v:
European Heart Journal Cardiovascular Imaging
Aims Pulmonary arterial hypertension (PAH) is a progressive condition with high mortality. Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target individual cardiac structures and have diagnostic and prognostic utility but
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::818bcf1bf6941760819926bbc1ede069
https://doi.org/10.1093/ehjci/jeaa001
https://doi.org/10.1093/ehjci/jeaa001
Publikováno v:
Multimedia Tools and Applications
Multimedia Tools and Applications, 2020, 79 (31-32), pp.23071-23092. ⟨10.1007/s11042-020-09095-y⟩
Multimedia Tools and Applications, Springer Verlag, 2020, 79 (31-32), pp.23071-23092. ⟨10.1007/s11042-020-09095-y⟩
Multimedia Tools and Applications, 2020, 79 (31-32), pp.23071-23092. ⟨10.1007/s11042-020-09095-y⟩
Multimedia Tools and Applications, Springer Verlag, 2020, 79 (31-32), pp.23071-23092. ⟨10.1007/s11042-020-09095-y⟩
International audience; In the last few years, there is a growing interest in multilinear subspace learning for dimensionality reduction of multidimensional data. In this paper, we proposed a multimodal 2D + 3D face verification system based on Multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbfd7fd24c8fa001f9ea3ccee19dc047
https://hal.science/hal-03321559
https://hal.science/hal-03321559
Publikováno v:
IEEE Signal Processing Letters. 25:333-337
This letter proposes a low-rank regularized heterogeneous tensor decomposition (LRRHTD) algorithm for subspace clustering, in which various constrains in different modes are incorporated to enhance the robustness of the proposed model. Specifically,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::746c1029580a75dcba5b2825689a6451
https://doi.org/10.1109/lsp.2017.2748604
https://doi.org/10.1109/lsp.2017.2748604
Publikováno v:
Neurocomputing. 275:888-896
Recently, the problem of extracting tensor object feature is studied and a very elegant solution, multilinear principal component analysis (MPCA), is proposed, which is motivated as a tool for tensor object dimension reduction and feature extraction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::980f164a44443078bbecf80ada06dddd
https://doi.org/10.1016/j.neucom.2017.08.070
https://doi.org/10.1016/j.neucom.2017.08.070
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.