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Probably certifiably correct k-means clustering
First, we prove that Peng and Wei's semidefinite relaxation of k-means is tight with high probability under a distribution of planted clusters called the stochastic ball model.
Learning Boolean functions with concentrated spectra
This paper discusses the theory and application of learning Boolean functions that are concentrated in the Fourier domain.
On the tightness of an SDP relaxation of k-means
Recently, Awasthi et al. introduced an SDP relaxation of the $k$-means problem in $\mathbb R^m$.
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