We consider the problem of learning underlying tree structure from noisy, mixed data obtained from a linear model. To achieve this, we use the expectation maximization algorithm combined with Chow-Liu minimum spanning tree algorithm. This algorithm is sub-optimal, but has low complexity and is applicable to model selection problems through any linear model.
@article{arxiv.1710.01838,
title = {Latent Tree Approximation in Linear Model},
author = {Navid Tafaghodi Khajavi},
journal= {arXiv preprint arXiv:1710.01838},
year = {2017}
}