Multiscale network analysis through tail-greedy bottom-up approximation, with applications in neuroscience

Kang, Xinyu, Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X, Chu, Catherine, Kramer, Mark and Kolaczyk, Eric D. (2018) Multiscale network analysis through tail-greedy bottom-up approximation, with applications in neuroscience. 2017 51st Asilomar Conference on Signals, Systems, and Computers. pp. 1549-1554. ISSN 2576-2303

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Abstract

We propose the TGUH (Tail-Greedy Unbalanced Haar) transform for networks, which results in an orthonormal, adaptive decomposition of the network adjacency matrix into Haar-wavelet like components. The `tail-greediness' of the algorithm - indicating multiple greedy steps are taken in a single pass through the data - enables both fast computation and consistent estimation of network signals. We focus on development of our multiscale network decomposition and a corresponding method for network signal denoising. Moreover, we establish consistency of our resulting denoising methodology, present numerical simulations illustrating compression, and illustrate through application to signals on diffusion tensor imaging (DTI) networks.

Item Type:	Article
Official URL:	https://ieeexplore.ieee.org/xpl/mostRecentIssue.js...
Additional Information:	© 2017 IEEE
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	20 Aug 2018 09:47
Last Modified:	11 Dec 2024 21:42
Projects:	EP/L014246/1, AFOSR, 1R01NS095369-01
Funders:	Engineering and Physical Sciences Research Council, National Institutes of Health
URI:	http://eprints.lse.ac.uk/id/eprint/90021

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