Sum-of-norms clustering does not separate nearby balls


Dunlap, A; Mourrat, J-C


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 the centers of the two balls is taken to be as large as $2\sqrt{2}$. In order to show this, we introduce and analyze a continuous version of sum-of-norms clustering, where the dataset is replaced by a general measure. In particular, we state and prove a local-global characterization of the clustering that seems to be new even in the case of discrete datapoints.


Dunlap, Alexander, and Jean-Christophe Mourrat. “Sum-of-norms clustering does not separate nearby balls.” Journal of Machine Learning Research 25, no. 143 (April 2024): 1–40.

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