TY - GEN
T1 - Sample Complexity of Joint Structure Learning
AU - Sihag, Saurabh
AU - Tajer, Ali
N1 - Publisher Copyright: © 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - This paper considers the problem of jointly recovering the structures of two graphical models with unknown edge structures. It is assumed that both graphs have the same number of nodes and a known subset of nodes have identical structures in both graphs. The classes of Ising models and Gaussian models are considered. For Ising models, the objective is to recover the connectivity of both graphs under an approximate recovery criterion. For Gaussian models, the objectives of edge structure recovery and inverse covariance estimation are considered. Information-theoretic bounds on the sample complexity for bounded probability of error under the aforementioned criteria are established and compared with the corresponding bounds on the sample complexity for recovering the graphs independently.
AB - This paper considers the problem of jointly recovering the structures of two graphical models with unknown edge structures. It is assumed that both graphs have the same number of nodes and a known subset of nodes have identical structures in both graphs. The classes of Ising models and Gaussian models are considered. For Ising models, the objective is to recover the connectivity of both graphs under an approximate recovery criterion. For Gaussian models, the objectives of edge structure recovery and inverse covariance estimation are considered. Information-theoretic bounds on the sample complexity for bounded probability of error under the aforementioned criteria are established and compared with the corresponding bounds on the sample complexity for recovering the graphs independently.
KW - Graphical models
KW - information-theoretic bounds
KW - joint model selection
KW - structural similarity
UR - https://www.scopus.com/pages/publications/85068966933
U2 - 10.1109/ICASSP.2019.8682199
DO - 10.1109/ICASSP.2019.8682199
M3 - Conference contribution
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5292
EP - 5296
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -