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Activity Number: 242 - Multiple Testing and Feature Selection
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #328681 Presentation
Title: Asymptotic Analysis of Large-Scale Multi-Relational Network Through Latent Variable Modeling
Author(s): Zhi Wang* and Xueying Tang and Jingchen Liu
Companies: Columbia University and Columbia University and Columbia University
Keywords: multi-relational network; latent variable modeling; KL divergence; large deviation; MLE
Abstract:

We establish consistency results on recovering multi-relational networks driven by a scoring function of low-dimensional latent attributes. We provide a non-asymptotic upper bound for the error probability of estimated distributions in terms of Kullback-Leibler divergence when the latent attributes are estimated by a maximum likelihood estimator or an $L_p$-penalized maximum likelihood estimator. Thanks to the non-asymptotic bound, consistency results hold even when the dimension of the latent space grows slowly with the sample size. Finite sample performance of the estimators is evaluated on simulated and real datasets.


Authors who are presenting talks have a * after their name.

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