Sparse Recovery With Orthogonal Matching Pursuit Under RIP

  • Tong Z
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Abstract

This paper presents a new analysis for the orthogonal matching pursuit (OMP) algorithm. It is shown that if the restricted isometry property (RIP) is satisfied at sparsity level O(k̅), then OMP can stably recover a -sparse signal in 2-norm under measurement noise. For compressed sensing applications, this result implies that in order to uniformly recover a -sparse signal in Rd, only O( lnd) random projections are needed. This analysis improves some earlier results on OMP depending on stronger conditions that can only be satisfied with Ω(2 lnd) or Ω(1.6 lnd) random projections.

Author-supplied keywords

  • Algorithm design and analysis
  • Approximation algorithms
  • Estimation theory
  • Matching pursuit algorithms
  • Noise
  • OMP algorithm
  • Optimization
  • RIP
  • Signal processing algorithms
  • compressed sensing
  • data compression
  • feature selection
  • greedy algorithms
  • iterative methods
  • orthogonal matching pursuit algorithm
  • restricted isometry property
  • signal restoration
  • sparse recovery
  • sparse signal recovery
  • sparsity level
  • statistical learning
  • time-frequency analysis

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Authors

  • Zhang Tong

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