3 research outputs found
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
We focus on the problem of estimating the change in the dependency structures
of two -dimensional Gaussian Graphical models (GGMs). Previous studies for
sparse change estimation in GGMs involve expensive and difficult non-smooth
optimization. We propose a novel method, DIFFEE for estimating DIFFerential
networks via an Elementary Estimator under a high-dimensional situation. DIFFEE
is solved through a faster and closed form solution that enables it to work in
large-scale settings. We conduct a rigorous statistical analysis showing that
surprisingly DIFFEE achieves the same asymptotic convergence rates as the
state-of-the-art estimators that are much more difficult to compute. Our
experimental results on multiple synthetic datasets and one real-world data
about brain connectivity show strong performance improvements over baselines,
as well as significant computational benefits.Comment: 20pages, 6 figures, 10 tables; at AISTAT 201
Learning the piece-wise constant graph structure of a varying Ising model
This work focuses on the estimation of multiple change-points in a
time-varying Ising model that evolves piece-wise constantly. The aim is to
identify both the moments at which significant changes occur in the Ising
model, as well as the underlying graph structures. For this purpose, we propose
to estimate the neighborhood of each node by maximizing a penalized version of
its conditional log-likelihood. The objective of the penalization is twofold:
it imposes sparsity in the learned graphs and, thanks to a fused-type penalty,
it also enforces them to evolve piece-wise constantly. Using few assumptions,
we provide two change-points consistency theorems. Those are the first in the
context of unknown number of change-points detection in time-varying Ising
model. Finally, experimental results on several synthetic datasets and a
real-world dataset demonstrate the performance of our method.Comment: 18 pages (9 pages for Appendix), 4 figures, 2 table
Differential Network Learning Beyond Data Samples
Learning the change of statistical dependencies between random variables is
an essential task for many real-life applications, mostly in the high
dimensional low sample regime. In this paper, we propose a novel differential
parameter estimator that, in comparison to current methods, simultaneously
allows (a) the flexible integration of multiple sources of information (data
samples, variable groupings, extra pairwise evidence, etc.), (b) being scalable
to a large number of variables, and (c) achieving a sharp asymptotic
convergence rate. Our experiments, on more than 100 simulated and two
real-world datasets, validate the flexibility of our approach and highlight the
benefits of integrating spatial and anatomic information for brain connectome
change discovery and epigenetic network identification.Comment: 9 pages of main draft; 25 pages of Appendix; 5 Tables ; 14 Figures ;
Learning of Structure Difference between Two Graphical Model