no code implementations • 24 Jan 2024 • Abdullah Karaaslanli, Selin Aviyente
In particular, we propose an optimization problem, where graph data is assumed to be smooth over the multiview graph and the topology of the individual views and that of the consensus graph are learned, simultaneously.
no code implementations • 9 Nov 2022 • Meiby Ortiz-Bouza, Selin Aviyente
While there has been a lot of work on graph clustering using the connectivity between the nodes, many real-world networks also have node attributes.
no code implementations • 31 Oct 2022 • Selin Aviyente, Alejandro Frangi, Erik Meijering, Arrate Muñoz-Barrutia, Michael Liebling, Dimitri Van De Ville, Jean-Christophe Olivo-Marin, Jelena Kovačević, Michael Unser
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.
no code implementations • 26 Sep 2022 • Abdullah Karaaslanli, Meiby Ortiz-Bouza, Tamanna T. K. Munia, Selin Aviyente
Results} The proposed approach is applied to electroencephalogram data collected during a study of error monitoring in the human brain.
no code implementations • 2 May 2022 • Meiby Ortiz-Bouza, Selin Aviyente
In this paper, we introduce a new multiplex community detection method that identifies communities that are common across layers as well as those that are unique to each layer.
1 code implementation • 19 Jan 2022 • Li Peide, Seyyid Emre Sofuoglu, Tapabrata Maiti, Selin Aviyente
Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities.
no code implementations • 23 Oct 2020 • Seyyid Emre Sofuoglu, Selin Aviyente
In particular, the anomaly detection problem is formulated as a robust lowrank + sparse tensor decomposition with a regularization term that minimizes the temporal variation of the sparse part, so that the extracted anomalies are temporally persistent.
no code implementations • 6 Oct 2020 • Seyyid Emre Sofuoglu, Selin Aviyente
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance and urban traffic monitoring.
1 code implementation • 15 Apr 2019 • Seyyid Emre Sofuoglu, Selin Aviyente
In this paper, we introduce a supervised learning approach for tensor classification based on the tensor-train model.
no code implementations • 2 Oct 2014 • Arash Golibagh Mahyari, Selin Aviyente
With the advances in high resolution neuroimaging, there has been a growing interest in the detection of functional brain connectivity.