@inproceedings{5916,
  title = {Approximation Algorithms for Tensor Clustering},
  booktitle = {Algorithmic Learning Theory: 20th International Conference},
  abstract = {We present the first (to our knowledge) approximation algo-
  rithm for tensor clustering—a powerful generalization to basic 1D clustering. Tensors are increasingly common in modern applications dealing
  with complex heterogeneous data and clustering them is a fundamental
  tool for data analysis and pattern discovery. Akin to their 1D cousins,
  common tensor clustering formulations are NP-hard to optimize. But,
  unlike the 1D case no approximation algorithms seem to be known. We
  address this imbalance and build on recent co-clustering work to derive
  a tensor clustering algorithm with approximation guarantees, allowing
  metrics and divergences (e.g., Bregman) as objective functions. Therewith, we answer two open questions by Anagnostopoulos et al. (2008).
  Our analysis yields a constant approximation factor independent of data
  size; a worst-case example shows this factor to be tight for Euclidean
  co-clustering. However, empirically the approximation factor is observed
  to be conservative, so our method can also be used in practice.},
  pages = {368-383},
  editors = {Gavalda, R. , G. Lugosi, T. Zeugmann, S. Zilles},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {2009},
  author = {Jegelka, S. and Sra, S. and Banerjee, A.},
  doi = {10.1007/978-3-642-04414-4_30},
  month_numeric = {10}
}