Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Aviyente, Selin"'
Autor:
Karaaslanli, Abdullah, Aviyente, Selin
Graph topology inference, i.e., learning graphs from a given set of nodal observations, is a significant task in many application domains. Existing approaches are mostly limited to learning a single graph assuming that the observed data is homogeneou
Externí odkaz:
http://arxiv.org/abs/2401.13769
Autor:
Ortiz-Bouza, Meiby, Aviyente, Selin
Many real-world systems can be represented as graphs where the different entities in the system are presented by nodes and their interactions by edges. An important task in studying large datasets with graphical structure is graph clustering. While t
Externí odkaz:
http://arxiv.org/abs/2211.04634
Autor:
Aviyente, Selin, Frangi, Alejandro, Meijering, Erik, Muñoz-Barrutia, Arrate, Liebling, Michael, Van De Ville, Dimitri, Olivo-Marin, Jean-Christophe, Kovačević, Jelena, Unser, Michael
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biol
Externí odkaz:
http://arxiv.org/abs/2210.17346
Objective: In recent years, the functional connectivity of the human brain has been studied with graph theoretical tools. One such approach is community detection which is fundamental for uncovering the localized networks. Existing methods focus on n
Externí odkaz:
http://arxiv.org/abs/2209.12779
Autor:
Ortiz-Bouza, Meiby, Aviyente, Selin
Publikováno v:
IEEE Access, vol. 12, pp. 6423-6436, 2024
Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necess
Externí odkaz:
http://arxiv.org/abs/2205.00626
Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics
Externí odkaz:
http://arxiv.org/abs/2201.07683
Autor:
Moser, Jason S., Munia, Tamanna T.K., Louis, Courtney C., Anderson, Grace E., Aviyente, Selin
Publikováno v:
In International Journal of Psychophysiology March 2024 197
Autor:
Sofuoglu, Seyyid Emre, Aviyente, Selin
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance, and urban traffic monitoring. Existing anomaly detection methods are most suited for point
Externí odkaz:
http://arxiv.org/abs/2010.12633
Autor:
Sofuoglu, Seyyid Emre, Aviyente, Selin
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance and urban traffic monitoring. In the case of urban traffic data, anomalies refer to unusual
Externí odkaz:
http://arxiv.org/abs/2010.02889
Autor:
Sofuoglu, Seyyid Emre, Aviyente, Selin
With the advances in data acquisition technology, tensor objects are collected in a variety of applications including multimedia, medical and hyperspectral imaging. As the dimensionality of tensor objects is usually very high, dimensionality reductio
Externí odkaz:
http://arxiv.org/abs/1911.01591