Clustering, Hamming Embedding, Generalized LSH and the Max Norm

Behnam Neyshabur, Yury Makarychev, Nathan Srebro

ALT 2014 pp. 306-320

doi:10.1007/978-3-319-11662-4_22 /alt/2014/neyshabur2014alt-clustering/

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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Cite

Text

Neyshabur et al. "Clustering, Hamming Embedding, Generalized LSH and the Max Norm." International Conference on Algorithmic Learning Theory, 2014. doi:10.1007/978-3-319-11662-4_22

Markdown

[Neyshabur et al. "Clustering, Hamming Embedding, Generalized LSH and the Max Norm." International Conference on Algorithmic Learning Theory, 2014.](https://mlanthology.org/alt/2014/neyshabur2014alt-clustering/) doi:10.1007/978-3-319-11662-4_22

BibTeX

@inproceedings{neyshabur2014alt-clustering,
  title     = {{Clustering, Hamming Embedding, Generalized LSH and the Max Norm}},
  author    = {Neyshabur, Behnam and Makarychev, Yury and Srebro, Nathan},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {2014},
  pages     = {306-320},
  doi       = {10.1007/978-3-319-11662-4_22},
  url       = {https://mlanthology.org/alt/2014/neyshabur2014alt-clustering/}
}