Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recovery of a mixture of Gaussians by sum-of-norms clustering   Sum-of-norms clustering is a method for assigning $n$ points in
$\mathbb{R}^d$ to $K$ clusters, $1\le K\le n$, using convex optimization.
Recently, Panahi et al.\ proved that sum-of-norms clustering is guaranteed to
recover a mixture of Gaussians under the restriction that the number of samples
is not too large. The purpose of this note is to lift this restriction, i.e.,
show that sum-of-norms clustering with equal weights can recover a mixture of
Gaussians even as the number of samples tends to infinity. Our proof relies on
an interesting characterization of clusters computed by sum-of-norms clustering
that was developed inside a proof of the agglomeration conjecture by Chiquet et
al. Because we believe this theorem has independent interest, we restate and
reprove the Chiquet et al.\ result herein.

18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex