Latent tree approximation in linear model

Navid Tafaghodi Khajavi

ICASSP 2017accepted0 citations

Latent tree approximation in linear model

Abstract

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.

BibTeX

@inproceedings{icassp2017_latenttreeapprox,
  title = {Latent tree approximation in linear model},
  author = {Navid Tafaghodi Khajavi},
  booktitle = {ICASSP 2017},
  year = {2017}
}