Learning Tree Structures from Noisy Data

We provide high-probability sample complexity guarantees for exact structure recovery of tree-structured graphical models, when only noisy observations of the respective vertex emissions are available. We assume that the hidden variables follow either an Ising model or a Gaussian graphical model, and the observables are noise-corrupted versions of the hidden variables: We consider multiplicative 1 binary noise for Ising models, and additive Gaussian noise for Gaussian models. Such hidden models arise naturally in a variety of applications such as physics, biology, computer science, and finance. We study the impact of measurement noise on the task of learning the underlying tree structure via the well-known Chow-Liu algorithm, and provide formal sample complexity guarantees for exact recovery. In particular, for a tree with p vertices and probability of failure delta > 0, we show that the number of necessary samples for exact structure recovery is of the order of O(log(p/delta)) for Ising models (which remains the same as in the noiseless case), and O(polylog(p/delta)) for Gaussian models.