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info.json
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{
"abstract": "Sum-of-norms clustering is a method for assigning $n$ points in $\\mathbf{R}^d$ to $K$ clusters, $1\\le K\\le n$, using convex optimization. Recently, Panahi (2017) 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, that is, show that sum-of-norms clustering 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. (2017). Because we believe this theorem has independent interest, we restate and reprove the Chiquet et al. (2017) result herein.",
"authors": [
"Tao Jiang",
"Stephen Vavasis",
"Chen Wen Zhai"
],
"emails": [
"tj293@cornell.edu",
"vavasis@uwaterloo.ca",
"zhai@mit.edu"
],
"id": "19-218",
"issue": 225,
"pages": [
1,
16
],
"title": "Recovery of a Mixture of Gaussians by Sum-of-Norms Clustering",
"volume": 21,
"year": 2020
}