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We introduce a model-free relax-and-round algorithm for k-means clustering based on a semidefinite relaxation due to Peng and Wei.
Rank-reducibility of a symmetric matrix and sampling theory of minimum trace factor analysis
Alexander Shapiro · 1982
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On the rank of extreme matrices in semidefinite programs and the multiplicity of optimal eigenvalues
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A probabilistic analysis of em for mixtures of separated, spherical gaussians
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Approximating k-means-type clustering via semidefinite programming
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Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Relax, no need to round: Integrality of clustering formulations
P. Awasthi, A. Bandeira, M. Charikar, R. Krishnaswamy, S. Villar, and R. Ward · 2015
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Probably certifiably correct k-means clustering, 2015
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Recovery guarantees for exemplar-based clustering
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Implementation of the k k -means semidefinite program, 2016
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