Work

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

Year: 2017

Type: conference-paper

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.

Source: Asilomar Conference on Signals, Systems and Computers (ACSSC)

DOI: 10.1109/acssc.2017.8335617

Authors: Xinyu Kang, Piotr Fryzlewicz, Catherine Chu, Mark Kramer, Eric D. Kolaczyk

Institutions: Boston University, London School of Economics and Political Science, Massachusetts General Hospital

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, Engineering and Physical Sciences Research Council, Air Force Office of Scientific Research

Award: New challenges in time series analysis