Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixture of Conditional Gaussian Graphical Models for unlabelled
  heterogeneous populations in the presence of co-factors   Conditional correlation networks, within Gaussian Graphical Models (GGM), are
widely used to describe the direct interactions between the components of a
random vector. In the case of an unlabelled Heterogeneous population,
Expectation Maximisation (EM) algorithms for Mixtures of GGM have been proposed
to estimate both each sub-population's graph and the class labels. However, we
argue that, with most real data, class affiliation cannot be described with a
Mixture of Gaussian, which mostly groups data points according to their
geometrical proximity. In particular, there often exists external co-features
whose values affect the features' average value, scattering across the feature
space data points belonging to the same sub-population. Additionally, if the
co-features' effect on the features is Heterogeneous, then the estimation of
this effect cannot be separated from the sub-population identification. In this
article, we propose a Mixture of Conditional GGM (CGGM) that subtracts the
heterogeneous effects of the co-features to regroup the data points into
sub-population corresponding clusters. We develop a penalised EM algorithm to
estimate graph-sparse model parameters. We demonstrate on synthetic and real
data how this method fulfils its goal and succeeds in identifying the
sub-populations where the Mixtures of GGM are disrupted by the effect of the
co-features.

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The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
1
The categories of this paper are: cs
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph