Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Konstantinos Slavakis"'
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 1073-1088 (2024)
This paper introduces a novel kernel regression framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by manifold learning, MultiL-KRIM models data features as a point-
Externí odkaz:
https://doaj.org/article/434b2a08b52b4809bae4bb80d0d5ed60
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 2, Pp 67-84 (2021)
This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion fo
Externí odkaz:
https://doaj.org/article/8f51f7bb7a254092b4f80807399cb5a5
This paper introduces an efficient multi-linear non- parametric (kernel-based) approximation framework for data regression and imputation, and its application to dynamic magnetic-resonance imaging (dMRI). Data features are assumed to reside in or clo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d05a2b889cb36b44175de789428076e
This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the probability density function of the outliers. The proposed online an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0eab13a3958ff39903fbbdae1f497028
https://doi.org/10.36227/techrxiv.21376458
https://doi.org/10.36227/techrxiv.21376458
This study addresses the problem of selecting dynamically, at each time instance, the “optimal” p-norm to combat outliers in linear adaptive filtering without any knowledge on the potentially timevarying probability density function of the outlie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::615762d66f460a6c31627f0b880ab71a
Autor:
Loris Cannelli, Gaurav N. Shetty, Leslie Ying, Gesualdo Scutari, Ukash Nakarmi, Konstantinos Slavakis
This paper introduces a non-parametric kernel-based modeling framework for imputation by regression on data that are assumed to lie close to an unknown-to-the-user smooth manifold in a Euclidean space. The proposed framework, coined kernel regression
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8acfa99d5942111d2ea7d4d3c342fa8b
https://doi.org/10.36227/techrxiv.14813673.v2
https://doi.org/10.36227/techrxiv.14813673.v2
Autor:
Konstantinos Slavakis
Publikováno v:
IEEE Transactions on Signal Processing. 67:2868-2883
This paper introduces the stochastic Fej\'{e}r-monotone hybrid steepest descent method (S-FM-HSDM) to solve affinely constrained and composite convex minimization tasks. The minimization task is not known exactly; noise contaminates the information a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bb5d8983f9d2fa7fd46e55295927d56
https://doi.org/10.1109/tsp.2019.2907257
https://doi.org/10.1109/tsp.2019.2907257
Publikováno v:
EUSIPCO
This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem. The proposed methodology exploits simple tangent-space geometries of manifolds in reproducing kernel Hilbe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::baa04c7e21aef75183b3f733a68bbbff
https://doi.org/10.23919/eusipco47968.2020.9287848
https://doi.org/10.23919/eusipco47968.2020.9287848
Autor:
Konstantinos Slavakis, Masahiro Yukawa
Publikováno v:
ICASSP
This paper introduces a non-parametric learning framework to combat outliers in online, multi-output, and nonlinear regression tasks. A hierarchical-optimization problem underpins the learning task: Search in a reproducing kernel Hilbert space (RKHS)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d34291b0d507119c8023944febf6fb2
https://doi.org/10.36227/techrxiv.13110893.v1
https://doi.org/10.36227/techrxiv.13110893.v1
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 2, Pp 67-84 (2021)
This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e0d219f824c9423ee6ebe3eaeef7a64
https://doi.org/10.36227/techrxiv.12725369
https://doi.org/10.36227/techrxiv.12725369