TY - GEN
T1 - Graph topology recovery for regular and irregular graphs
AU - Varma, Rohan
AU - Chen, Siheng
AU - Kovačević, Jelena
PY - 2018/3/9
Y1 - 2018/3/9
N2 - In this paper, we study the recovery of the graph topology or structure. We first extend our previous work on graph signal recovery to present a joint graph signal and structure recovery framework. By doing this, we allow the algorithm to learn a graph structure from noisy and incomplete graph signals and recover the graph signals at the same time. In this paper, we particularly focus on the specific subproblem of graph structure learning and develop algorithms towards this problem and analyze them. We briefly study the implications when the underlying true graph structure is irregular or regular. Finally, we validate the proposed methods for both synthetic data and the real-world recovery problem of semi-supervised digit-image classification.
AB - In this paper, we study the recovery of the graph topology or structure. We first extend our previous work on graph signal recovery to present a joint graph signal and structure recovery framework. By doing this, we allow the algorithm to learn a graph structure from noisy and incomplete graph signals and recover the graph signals at the same time. In this paper, we particularly focus on the specific subproblem of graph structure learning and develop algorithms towards this problem and analyze them. We briefly study the implications when the underlying true graph structure is irregular or regular. Finally, we validate the proposed methods for both synthetic data and the real-world recovery problem of semi-supervised digit-image classification.
KW - discrete signal processing on graphs
KW - graph structure recovery
KW - sampling
KW - signal recovery
UR - http://www.scopus.com/inward/record.url?scp=85051118561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051118561&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2017.8313202
DO - 10.1109/CAMSAP.2017.8313202
M3 - Conference contribution
AN - SCOPUS:85051118561
T3 - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
SP - 1
EP - 5
BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Y2 - 10 December 2017 through 13 December 2017
ER -