Single Index Latent Variable Models for Network Topology Inference
Machine Learning
2018-07-03 v1 Machine Learning
Abstract
A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex system of interacting entities. This formulation jointly estimates non-linearities in the underlying data generation, the direct interactions between measured entities, and the indirect effects of unmeasured processes on the observed data. The learning is posed as regularized empirical risk minimization. Details of the algorithm for learning the model are outlined. Experiments demonstrate the performance of the learned model on real data.
Cite
@article{arxiv.1807.00002,
title = {Single Index Latent Variable Models for Network Topology Inference},
author = {Jonathan Mei and José M. F. Moura},
journal= {arXiv preprint arXiv:1807.00002},
year = {2018}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1705.03536