Web: http://arxiv.org/abs/2104.13753

Jan. 31, 2022, 2:11 a.m. | Alexander Dunlap, Jean-Christophe Mourrat

cs.LG updates on arXiv.org arxiv.org

Sum-of-norms clustering is a popular convexification of $K$-means clustering.
We show that, if the dataset is made of a large number of independent random
variables distributed according to the uniform measure on the union of two
disjoint balls of unit radius, and if the balls are sufficiently close to one
another, then sum-of-norms clustering will typically fail to recover the
decomposition of the dataset into two clusters. As the dimension tends to
infinity, this happens even when the distance between …

arxiv clustering

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