Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Bayesian Unsupervised Learning   This paper is about variable selection, clustering and estimation in an
unsupervised high-dimensional setting. Our approach is based on fitting
constrained Gaussian mixture models, where we learn the number of clusters $K$
and the set of relevant variables $S$ using a generalized Bayesian posterior
with a sparsity inducing prior. We prove a sparsity oracle inequality which
shows that this procedure selects the optimal parameters $K$ and $S$. This
procedure is implemented using a Metropolis-Hastings algorithm, based on a
clustering-oriented greedy proposal, which makes the convergence to the
posterior very fast.

17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas