Corpus ID: 690816

Approximation Algorithms for Bregman Co-clustering and Tensor Clustering

@article{Jegelka2008ApproximationAF,
  title={Approximation Algorithms for Bregman Co-clustering and Tensor Clustering},
  author={S. Jegelka and S. Sra and A. Banerjee},
  journal={ArXiv},
  year={2008},
  volume={abs/0812.0389}
}
  • S. Jegelka, S. Sra, A. Banerjee
  • Published 2008
  • Computer Science, Mathematics
  • ArXiv
  • In the past few years powerful generalizations to the Euclidean k-means problem have been made, such as Bregman clustering [7], co-clustering (i.e., simultaneous clustering of rows and columns of an input matrix) [9,18], and tensor clustering [8,34]. Like k-means, these more general problems also suffer from the NP-hardness of the associated optimization. Researchers have developed approximation algorithms of varying degrees of sophistication for k-means, k-medians, and more recently also for… CONTINUE READING
    Approximation Algorithms for Tensor Clustering
    33
    New results on information theoretic clustering
    2
    Information theoretical clustering is hard to approximate
    Information theoretical clustering is hard to approximate

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