TY - GEN

T1 - LEARNING SPARSE GRAPHS UNDER SMOOTHNESS PRIOR

AU - Chepuri, Sundeep Prabhakar

AU - Liu, Sijia

AU - Leus, Geert

AU - Hero, Alfred O.

N1 - KAUST Repository Item: Exported on 2022-06-23
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported in part by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700 and the US Army Research Office under grant W911NF-15-1-0479.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2017

Y1 - 2017

N2 - In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we parameterize the graph topology using an edge sampling function. That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which provides an explicit handle to control the sparsity level of the graph. We solve the sparse graph learning problem given some training data in both the noiseless and noisy settings. Given the true smooth data, the posed sparse graph learning problem can be solved optimally and is based on simple rank ordering. Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.

AB - In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we parameterize the graph topology using an edge sampling function. That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which provides an explicit handle to control the sparsity level of the graph. We solve the sparse graph learning problem given some training data in both the noiseless and noisy settings. Given the true smooth data, the posed sparse graph learning problem can be solved optimally and is based on simple rank ordering. Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.

UR - http://hdl.handle.net/10754/679281

UR - http://ieeexplore.ieee.org/document/7953410/

UR - http://www.scopus.com/inward/record.url?scp=85023780344&partnerID=8YFLogxK

U2 - 10.1109/icassp.2017.7953410

DO - 10.1109/icassp.2017.7953410

M3 - Conference contribution

SN - 9781509041176

SP - 6508

EP - 6512

BT - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

ER -