Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tree-based Node Aggregation in Sparse Graphical Models   High-dimensional graphical models are often estimated using regularization
that is aimed at reducing the number of edges in a network. In this work, we
show how even simpler networks can be produced by aggregating the nodes of the
graphical model. We develop a new convex regularized method, called the
tree-aggregated graphical lasso or tag-lasso, that estimates graphical models
that are both edge-sparse and node-aggregated. The aggregation is performed in
a data-driven fashion by leveraging side information in the form of a tree that
encodes node similarity and facilitates the interpretation of the resulting
aggregated nodes. We provide an efficient implementation of the tag-lasso by
using the locally adaptive alternating direction method of multipliers and
illustrate our proposal's practical advantages in simulation and in
applications in finance and biology.

30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph