Dimensionality-reduced subspace clustering

@article{Heckel2015DimensionalityreducedSC,
  title={Dimensionality-reduced subspace clustering},
  author={Reinhard Heckel and Michael Tschannen and H. B{\"o}lcskei},
  journal={ArXiv},
  year={2015},
  volume={abs/1507.07105}
}
  • Reinhard Heckel, Michael Tschannen, H. Bölcskei
  • Published 2015
  • Computer Science, Mathematics
  • ArXiv
  • Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, whose number, orientations, and dimensions are all unknown. In practice one may have access to dimensionality-reduced observations of the data only, resulting, e.g., from undersampling due to complexity and speed constraints on the acquisition device or mechanism. More pertinently, even if the high-dimensional data set is available it is often… CONTINUE READING
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