TY - GEN
T1 - Smooth-projected neighborhood pursuit for high-dimensional nonparanormal graph estimation
AU - Zhao, Tuo
AU - Roeder, Kathryn
AU - Liu, Han
PY - 2012
Y1 - 2012
N2 - We introduce a new learning algorithm, named smooth-projected neighborhood pursuit, for estimating high dimensional undirected graphs. In particularly, we focus on the nonparanormal graphical model and provide theoretical guarantees for graph estimation consistency. In addition to new computational and theoretical analysis, we also provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Numerical results on both synthetic and real datasets are provided to support our theory.
AB - We introduce a new learning algorithm, named smooth-projected neighborhood pursuit, for estimating high dimensional undirected graphs. In particularly, we focus on the nonparanormal graphical model and provide theoretical guarantees for graph estimation consistency. In addition to new computational and theoretical analysis, we also provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Numerical results on both synthetic and real datasets are provided to support our theory.
UR - http://www.scopus.com/inward/record.url?scp=84877739972&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877739972&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84877739972
SN - 9781627480031
T3 - Advances in Neural Information Processing Systems
SP - 162
EP - 170
BT - Advances in Neural Information Processing Systems 25
T2 - 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Y2 - 3 December 2012 through 6 December 2012
ER -