Abstract
We study the convex relaxation of clustering and hamming embedding, focusing on the asymmetric case (co-clustering and asymmetric hamming embedding), understanding their relationship to LSH as studied by Charikar (2002) and to the max-norm ball, and the differences between their symmetric and asymmetric versions.
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Neyshabur, B., Makarychev, Y., Srebro, N. (2014). Clustering, Hamming Embedding, Generalized LSH and the Max Norm. In: Auer, P., Clark, A., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2014. Lecture Notes in Computer Science(), vol 8776. Springer, Cham. https://doi.org/10.1007/978-3-319-11662-4_22
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DOI: https://doi.org/10.1007/978-3-319-11662-4_22
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