Robustness of SDPs for Partial Recovery of Clustering Subgaussian Mixtures
نویسندگان
چکیده
In this paper, we examine the robustness of a relax-and-round k-means clustering procedure, a method for clustering subgaussian mixtures using semidefinite programming first introduced in [MVW16]. We are interested in the robustness of the algorithm when there is an adversarial corruption of N points each through distance at most R0. We show that under such corruption this specific algorithm well-approximates the center of the subgaussians.
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