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Online Graph Topology Inference with Kernels For Brain Connectivity Estimation
- Source :
- ICASSP, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, France. pp.1200-1204, ⟨10.1109/ICASSP40776.2020.9053148⟩
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- International audience; In graph signal processing, there are often settings where the graph topology is not known beforehand and has to be estimated from data. Moreover, some graphs can be dynamic, such as brain activity supported by neurons or brain regions. This paper focuses on estimating in an online and adaptive manner a network structure capturing the non-linear dependencies among streaming graph signals in the form of a possibly directed, adjacency matrix. By projecting data into a higher-or infinite-dimension space, we focus on capturing nonlinear relationships between agents. In order to mitigate the increasing number of data points, we employ kernel dictionaries. Finally, we run a series of tests in order to experimentally illustrate the usefulness of our kernel-based approach on biomedical data, on which we obtain results comparable to state-of-the-art methods.
- Subjects :
- Topology inference
Theoretical computer science
Adaptive algorithm
Computer science
reproducing kernel
Inference
020206 networking & telecommunications
02 engineering and technology
Graph
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Nonlinear system
Kernel (linear algebra)
Data point
adaptive algorithm
0202 electrical engineering, electronic engineering, information engineering
Topological graph theory
020201 artificial intelligence & image processing
Adjacency matrix
graph signal processing
brain connectivity estimation
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Accession number :
- edsair.doi.dedup.....c3f7142f4255da8af87f61908aca68c5