Information Theoretic Bounds for Tensor Rank Minimization over Finite Fields

  title={Information Theoretic Bounds for Tensor Rank Minimization over Finite Fields},
  author={Amin Emad and Olgica Milenkovic},
  journal={2011 IEEE Global Telecommunications Conference - GLOBECOM 2011},
We consider the problem of noiseless and noisy low- rank tensor completion from a set of random linear measurements. In our derivations, we assume that the entries of the tensor belong to a finite field of arbitrary size and that reconstruction is based on a rank minimization framework. The derived results show that the smallest number of measurements needed for exact reconstruction is upper bounded by the product of the rank, the order, and the dimension of a cubic tensor. Furthermore, this… CONTINUE READING

From This Paper

Topics from this paper.
2 Citations
11 References
Similar Papers


Publications citing this paper.
Showing 1-2 of 2 extracted citations


Publications referenced by this paper.
Showing 1-10 of 11 references

Protein-Protein Int eraction Prediction using Non-Linear Matrix Completion Methods

  • A. Emad, W. Dai, O. Milenkovic
  • to be presented at RECOMB’2011, Vancouver…
  • 2011
1 Excerpt

Guaranteed MinimumRank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

  • M. Fazel B. Recht, P. A. Parrilo
  • SIAM Rev .
  • 2010

Probability Inequalities for Sums of Bo unded Random Variables

  • W. Hoeffding
  • J. Amer. Statist. Assoc.,
  • 1963
1 Excerpt

Similar Papers

Loading similar papers…