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of 48
pro vyhledávání: '"A. Hippert Ferrer"'
Graphical models and factor analysis are well-established tools in multivariate statistics. While these models can be both linked to structures exhibited by covariance and precision matrices, they are generally not jointly leveraged within graph lear
Externí odkaz:
http://arxiv.org/abs/2210.11950
Publikováno v:
IEEE Transactions on Signal Processing, vol. 71, pp. 1669-1682, 2023
This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian mixture models, has shown interesting properties when compared to other
Externí odkaz:
http://arxiv.org/abs/2201.12020
This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices. It relies on the observed-data likelihood within an expectation-maximization algorithm. This approach is compared to two
Externí odkaz:
http://arxiv.org/abs/2110.10011
This paper tackles the problem of robust covariance matrix estimation when the data is incomplete. Classical statistical estimation methodologies are usually built upon the Gaussian assumption, whereas existing robust estimation ones assume unstructu
Externí odkaz:
http://arxiv.org/abs/2107.10505
Publikováno v:
In Signal Processing June 2022 195
Publikováno v:
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters, IEEE-Institute of Electrical and Electronics Engineers, 2021, 18 (12), pp.2157-2161. ⟨10.1109/LGRS.2020.3015149⟩
IEEE Geoscience and Remote Sensing Letters, IEEE-Institute of Electrical and Electronics Engineers, 2021, 18 (12), pp.2157-2161. ⟨10.1109/LGRS.2020.3015149⟩
Missing data is a critical pitfall in the investigation of remotely sensed displacement measurement because it prevents from a full understanding of the physical phenomenon under observation. In the sight of reconstructing incomplete displacement dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e7aeadf0a7b81aa03289136e6ffcbdb
https://doi.org/10.1109/lgrs.2020.3015149
https://doi.org/10.1109/lgrs.2020.3015149
Akademický článek
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This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian mixture models, has shown interesting properties when compared to other
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0863a03fb2841866a3258424ee589192
Publikováno v:
Signal Processing
Signal Processing, 2021, 188, pp.108195. ⟨10.1016/j.sigpro.2021.108195⟩
Signal Processing, Elsevier, 2021, 188, pp.108195. ⟨10.1016/j.sigpro.2021.108195⟩
Signal Processing, 2021, 188, pp.108195. ⟨10.1016/j.sigpro.2021.108195⟩
Signal Processing, Elsevier, 2021, 188, pp.108195. ⟨10.1016/j.sigpro.2021.108195⟩
International audience; In this paper, robust mean and covariance matrix estimation are considered in the context of mixed-effects models. Such models are widely used to analyze repeated measures data which arise in several signal processing applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b0b373a6df01ca690963a4aeb437a70
https://hal.univ-grenoble-alpes.fr/hal-03273077/file/Alex_SP_21.pdf
https://hal.univ-grenoble-alpes.fr/hal-03273077/file/Alex_SP_21.pdf
Publikováno v:
EGU General Assembly
EGU General Assembly, May 2020, Vienne, Austria
EGU General Assembly, May 2020, Vienne, Austria
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::63f2c6dd38cf20ede6e8b5db781127a9
https://hal.archives-ouvertes.fr/hal-02569192
https://hal.archives-ouvertes.fr/hal-02569192