24 citations · 39 across the 17 of their papers we have counts for
20 papers
Multiscale Community-Based Fingerprinting of Signed Functional Networks
Sema Athamnah, Selin Aviyente
Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or \textit{fingerprints}, that can identify individuals across repeated sessi…
Signed Graph Learning: Algorithms and Theory
Abdullah Karaaslanli, Bisakh Banerjee, Tapabrata Maiti +1
Real-world data is often represented through the relationships between data samples, forming a graph structure. In many applications, it is necessary to learn this graph structure…
Hypergraph Overlapping Community Detection for Brain Networks
Duc Vu, Selin Aviyente
Functional magnetic resonance imaging (fMRI) has been commonly used to construct functional connectivity networks (FCNs) of the human brain. TFCNs are primarily limited to quantify…
Learning Graph Filters for Structure-Function Coupling based Hub Node Identification
Meiby Ortiz-Bouza, Duc Vu, Abdullah Karaaslanli +1
Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure…
Discriminative community detection for multiplex networks
Meiby Ortiz-Bouza, Selin Aviyente
Multiplex networks have emerged as a promising approach for modeling complex systems, where each layer represents a different mode of interaction among entities of the same type. A…
Multiview Graph Learning with Consensus Graph
Abdullah Karaaslanli, Selin Aviyente
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 Optimal Graph Filters for Clustering of Attributed Graphs
Meiby Ortiz-Bouza, Selin Aviyente
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 studyin…
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
Selin Aviyente, Alejandro Frangi, Erik Meijering +6
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 i…
Community Detection in Multi-frequency EEG Networks
Abdullah Karaaslanli, Meiby Ortiz-Bouza, Tamanna T. K. Munia +1
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 fundamen…
Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization
Meiby Ortiz-Bouza, Selin Aviyente
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…
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data
Li Peide, Seyyid Emre Sofuoglu, Tapabrata Maiti +1
Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from m…
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data
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 mo…
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data
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 mon…
Graph Regularized Tensor Train Decomposition
Seyyid Emre Sofuoglu, Selin Aviyente
With the advances in data acquisition technology, tensor objects are collected in a variety of applications including multimedia, medical and hyperspectral imaging. As the dimensio…
Constrained Spectral Clustering for Dynamic Community Detection
Abdullah Karaaslanli, Selin Aviyente
Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the…
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis
Seyyid Emre Sofuoglu, Selin Aviyente
Higher-order data with high dimensionality arise in a diverse set of application areas such as computer vision, video analytics and medical imaging. Tensors provide a natural tool…
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA
Ali Zare, Alp Ozdemir, Mark A. Iwen +1
The widespread use of multisensor technology and the emergence of big data sets have brought the necessity to develop more versatile tools to represent higher-order data with multi…
Multiscale Analysis for Higher-order Tensors
Alp Ozdemir, Ali Zare, Mark A. Iwen +1
The widespread use of multisensor technology and the emergence of big datasets have created the need to develop tools to reduce, approximate, and classify large and multimodal data…
Simultaneous Sparse Approximation and Common Component Extraction using Fast Distributed Compressive Sensing
Arash Golibagh Mahyari, Selin Aviyente
Simultaneous sparse approximation is a generalization of the standard sparse approximation, for simultaneously representing a set of signals using a common sparsity model. Generali…
Identification of Dynamic functional brain network states Through Tensor Decomposition
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. Complex network theory has been proposed as…