Efficient and robust compressed sensing using optimized expander graphs

  • Authors:

  • Sina Jafarpour;Weiyu Xu;Babak Hassibi;Robert Calderbank

  • Affiliations:

  • Department of Computer Science, Princeton University, Princeton, NJ;Department of Electrical Engineering, California Institute of Technology, Pasadena, CA;Department of Electrical Engineering, California Institute of Technology, Pasadena, CA;Department of Electrical Engineering, Princeton University, Princeton, NJ

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2009

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Abstract

Expander graphs have been recently proposed to construct efficient compressed sensing algorithms. In particular, it has been shown that any n-dimensional vector that is k-sparse can be fully recovered using O(k log n) measurements and only O(k log n) simple recovery iterations. In this paper, we improve upon this result by considering expander graphs with expansion coefficient beyond 3/4 and show that, with the same number of measurements, only O(k) recovery iterations are required, which is a significant improvement when n is large. In fact, full recovery can be accomplished by at most 2k very simple iterations. The number of iterations can be reduced arbitrarily close to k, and the recovery algorithm can be implemented very efficiently using a simple priority queue with total recovery time O(n log(n/k)). We also show that by tolerating a small penalty on the number of measurements, and not on the number of recovery iterations, one can use the efficient construction of a family of expander graphs to come up with explicit measurement matrices for this method. We compare our result with other recently developed expander-graph-based methods and argue that it compares favorably both in terms of the number of required measurements and in terms of the time complexity and the simplicity of recovery. Finally, we will show how our analysis extends to give a robust algorithm that finds the position and sign of the k significant elements of an almost k-sparse signal and then, using very simple optimization techniques, finds a k-sparse signal which is close to the best k-term approximation of the original signal.