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Found 10 papers, 2 papers with code
Multiview Graph Learning with Consensus Graph
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.
Optimal Graph Filters for Clustering Attributed Graphs
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.
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
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.
Community Detection in Multi-frequency EEG Networks
Results} The proposed approach is applied to electroencephalogram data collected during a study of error monitoring in the human brain.
Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization
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.
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data
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.
Low-rank on Graphs plus Temporally Smooth Sparse Decomposition for Anomaly Detection in Spatiotemporal Data
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.
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance and urban traffic monitoring.
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis
In this paper, we introduce a supervised learning approach for tensor classification based on the tensor-train model.
Identification of Dynamic functional brain network states Through Tensor Decomposition
With the advances in high resolution neuroimaging, there has been a growing interest in the detection of functional brain connectivity.
