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Dynamic graph learn...
Dynamic graph learning: A structure-driven approach
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- Jiang, Bo (författare)
- North Carolina State University
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- Huang, Yuming (författare)
- North Carolina State University
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- Panahi, Ashkan, 1986 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Yu, Yiyi (författare)
- University of California
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- Krim, Hamid (författare)
- North Carolina State University
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- Smith, Spencer L. (författare)
- University of California
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(creator_code:org_t)
- 2021-01-15
- 2021
- Engelska.
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Ingår i: Mathematics. - : MDPI AG. - 2227-7390. ; 9:2, s. 1-20
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://www.mdpi.com...
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https://doi.org/10.3...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the nodes. The motivation of this work lies in the search for a connectome model which properly captures brain functionality across all regions of the brain, and possibly at individual neurons. We formulate it as an optimization problem, a quadratic objective functional and tensor information of observed node signals over short time intervals. The proper regularization constraints reflect the graph smoothness and other dynamics involving the underlying graph’s Laplacian, as well as the time evolution smoothness of the underlying graph. The resulting joint optimization is solved by a continuous relaxation of the weight parameters and an introduced novel gradient-projection scheme. While the work may be applicable to any time-evolving data set (e.g., fMRI), we apply our algorithm to a real-world dataset comprising recorded activities of individual brain cells. The resulting model is shown to be not only viable but also efficiently computable.
Ämnesord
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Graph signal processing
- Dynamic graph learning
- Sparse signal
- Convex optimization
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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