Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Clustering Gaussian Graphical Models   We derive an efficient method to perform clustering of nodes in Gaussian
graphical models directly from sample data. Nodes are clustered based on the
similarity of their network neighborhoods, with edge weights defined by partial
correlations. In the limited-data scenario, where the covariance matrix would
be rank-deficient, we are able to make use of matrix factors, and never need to
estimate the actual covariance or precision matrix. We demonstrate the method
on functional MRI data from the Human Connectome Project. A matlab
implementation of the algorithm is provided.

2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics