Imperial College London

ProfessorDaniloMandic

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6271d.mandic Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

813Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Stankovic:2020:10.1109/ICASSP40776.2020.9053641,
author = {Stankovic, L and Dakovic, M and Mandic, D and Brajovic, M and Scalzo-Dees, B and Constantinides, AG},
doi = {10.1109/ICASSP40776.2020.9053641},
pages = {5340--5344},
title = {A Low-Dimensionality Method for Data-Driven Graph Learning},
url = {http://dx.doi.org/10.1109/ICASSP40776.2020.9053641},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - © 2020 IEEE. In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian sparsity, with various additional constraints, followed by variations of the edge weights in the graph domain or the eigenvalues in the graph spectral domain. These domains are high-dimensional, since their dimension is at least equal to the order of the number of vertices. In this paper, we propose a numerically efficient method for estimating of the normalized Laplacian through its eigenvalues estimation and by promoting its sparsity. The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph corresponding to the the observed data. The accuracy of the results is tested on numerical example.
AU - Stankovic,L
AU - Dakovic,M
AU - Mandic,D
AU - Brajovic,M
AU - Scalzo-Dees,B
AU - Constantinides,AG
DO - 10.1109/ICASSP40776.2020.9053641
EP - 5344
PY - 2020///
SN - 1520-6149
SP - 5340
TI - A Low-Dimensionality Method for Data-Driven Graph Learning
UR - http://dx.doi.org/10.1109/ICASSP40776.2020.9053641
ER -