Matrix Completion and Low-Rank Matrix Recovery

  • Robert Qiu
  • Michael Wicks


This chapter is a natural development following Chap. 7. In other words, Chaps. 7 and 8 may be viewed as two parallel developments. In Chap. 7, compressed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.


Rank Function Convex Optimization Problem Ambiguity Function Phase Retrieval Restricted Isometry Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Robert Qiu
    • 1
  • Michael Wicks
    • 2
  1. 1.Tennessee Technological UniversityCookevilleUSA
  2. 2.UticaUSA

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