@article {jiangvavasiszhai,
title = {Recovery of a mixture of Gaussians by sum-of-norms clustering},
journal = {Journal of Machine Learning Research},
volume = {21},
year = {2020},
note = {Arxiv link: },
pages = {1-16},
abstract = {Sum-of-norms clustering is a method for assigning n points in Rd to K clusters, 1<=K<=n, using convex optimization. Recently, Panahi et al. (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.},
url = {https://jmlr.org/papers/volume21/19-218/19-218.pdf},
author = {Jiang, T. and Vavasis, S. and Zhai., C. W.}
}