Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Aref Miri Rekavandi"'
Autor:
Hao Tang, Aref Miri Rekavandi, Dharjinder Rooprai, Girish Dwivedi, Frank M. Sanfilippo, Farid Boussaid, Mohammed Bennamoun
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we
Externí odkaz:
https://doaj.org/article/9950d70f8b244a93ae00b1d71cf1c5df
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e821562519044e2a4ebcbd961e0c891
https://doi.org/10.1109/icassp49357.2023.10094931
https://doi.org/10.1109/icassp49357.2023.10094931
Autor:
Uzair Nadeem, Mohammed Bennamoun, Roberto Togneri, Ferdous Sohel, Aref Miri Rekavandi, Farid Boussaid
Publikováno v:
Pattern Recognition. 142:109655
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::000ae75b96845817d7c09cbcf12bafe4
https://doi.org/10.1016/j.patcog.2023.109655
https://doi.org/10.1016/j.patcog.2023.109655
Camera pose estimation has long relied on geometry-based approaches and sparse 2D-3D keypoint correspondences. With the advent of deep learning methods, the estimation of camera pose parameters (i.e., the six parameters that describe position and rot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75746cadd2e05cb440e72cc36d23088e
https://doi.org/10.36227/techrxiv.21951398
https://doi.org/10.36227/techrxiv.21951398
In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using the α− divergence for Gaussian Mixture Model (GMM). This approach is used in robust subspace detection when the number of parameters is kept small to avoid o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70ee657c57e33887141456b78e35b6ce
https://doi.org/10.36227/techrxiv.21432027
https://doi.org/10.36227/techrxiv.21432027
Publikováno v:
IEEE Transactions on Image Processing. 30:5017-5031
Robust variants of Wald, Rao and likelihood ratio (LR) tests for the detection of a signal subspace in a signal interference subspace corrupted by contaminated Gaussian noise are proposed in this paper. They are derived using the $\alpha -$ divergenc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5939edbea41966cebb4a167b1301fb89
https://doi.org/10.1109/tip.2021.3077139
https://doi.org/10.1109/tip.2021.3077139
Publikováno v:
IEEE transactions on bio-medical engineering. 69(10)
In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1c28349a5d9e684bafd83c9bf3daeac
https://pubmed.ncbi.nlm.nih.gov/35320080
https://pubmed.ncbi.nlm.nih.gov/35320080
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b596e89e1ce8bcaf82fce7203415f5b
https://doi.org/10.2139/ssrn.4273623
https://doi.org/10.2139/ssrn.4273623
Publikováno v:
ICIP
In this paper, a new robust principal component analysis (RPCA) method which enables us to exploit the main components of a given corrupted data with non Gaussian outliers is proposed. This method is based on the $\alpha-$divergence which is a parame
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6067abb295a5e4c096007a87a4cc342a
https://doi.org/10.1109/icip40778.2020.9190918
https://doi.org/10.1109/icip40778.2020.9190918
Publikováno v:
ICASSP
The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by colu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::610dd516312f1d434e7e38e98804c779
https://doi.org/10.1109/icassp40776.2020.9053748
https://doi.org/10.1109/icassp40776.2020.9053748