TY - GEN
T1 - On the minimization of sobolev norms of time-varying graph signals
T2 - 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
AU - Giraldo, Jhony H.
AU - Bouwmans, Thierry
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - The mathematical modeling of infectious diseases is a fundamental research field for the planning of strategies to contain outbreaks. The models associated with this field of study usually have exponential prior assumptions in the number of new cases, while the exploration of spatial data has been little analyzed in these models. In this paper, we model the number of new cases of the Coronavirus Disease 2019 (COVID-19) as a problem of reconstruction of time-varying graph signals. To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing. Our method outperforms state-of-the-art algorithms in two COVID-19 databases provided by Johns Hopkins University. In the same way, we prove the benefits of the convergence rate of the Sobolev reconstruction method by relying on the condition number of the Hessian associated with the underlying optimization problem of our method.
AB - The mathematical modeling of infectious diseases is a fundamental research field for the planning of strategies to contain outbreaks. The models associated with this field of study usually have exponential prior assumptions in the number of new cases, while the exploration of spatial data has been little analyzed in these models. In this paper, we model the number of new cases of the Coronavirus Disease 2019 (COVID-19) as a problem of reconstruction of time-varying graph signals. To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing. Our method outperforms state-of-the-art algorithms in two COVID-19 databases provided by Johns Hopkins University. In the same way, we prove the benefits of the convergence rate of the Sobolev reconstruction method by relying on the condition number of the Hessian associated with the underlying optimization problem of our method.
KW - COVID-19
KW - Signal reconstruction
KW - Sobolev norm
KW - Time-varying graph signals
UR - https://www.scopus.com/pages/publications/85096461983
U2 - 10.1109/MLSP49062.2020.9231810
DO - 10.1109/MLSP49062.2020.9231810
M3 - Conference contribution
AN - SCOPUS:85096461983
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PB - IEEE Computer Society
Y2 - 21 September 2020 through 24 September 2020
ER -
