Skip to main content

Uniqueness and Uncertainty

  • Chapter
  • First Online:

Abstract

We return to the basic problem (P 0), which is at the core of our discussion,

$${\left(P_o\right):\quad \min\limits_X \parallel \mathbf{X}\parallel_0 \,{\rm subject\,\, to} \mathbf \quad \mathbf{b}=\mathbf{A\mathbf{x}}}.$$

While we shall refer hereafter to this problem as our main goal, we stress that we are quite aware of its two major shortcomings in leading to any practical tool. 1. The equality requirement b = A X is too strict, as there are small chances for any vector b to be represented by a few columns from A. A better requirement would be one that allows for small deviation. 2. The sparsity measure is too sensitive to very small entries in X, and a better measure would adopt a more forgiving approach towards such small entries.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   49.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Further Reading

  1. A.R. Calderbank and P.W. Shor, Good quantum error-correcting codes exist, Phys. Rev. A, 54(2):1098–1105, August 1996.

    Article  Google Scholar 

  2. D.L. Donoho and M. Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization, Proc. of the National Academy of Sciences, 100(5):2197–2202, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  3. D.L. Donoho and X. Huo, Uncertainty principles and ideal atomic decomposition, IEEE Trans. On Information Theory, 47(7):2845–2862, 1999.

    Article  MathSciNet  Google Scholar 

  4. D.L. Donoho and P.B. Starck, Uncertainty principles and signal recovery, SIAM Journal on Applied Mathematics, 49(3):906–931, June, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  5. M. Elad and A.M. Bruckstein, A generalized uncertainty principle and sparse representation in pairs of bases, IEEE Trans. On Information Theory, 48:2558–2567, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  6. I.F. Gorodnitsky and B.D. Rao, Sparse signal reconstruction from limited data using FOCUSS: A re-weighted norm minimization algorithm, IEEE Trans. On Signal Processing, 45(3):600–616, 1997.

    Article  Google Scholar 

  7. S. Gurevich, R. Hadani, and N. Sochen, The finite harmonic oscillator and its associated sequences Proc. Natl. Acad. Sci. USA, 105(29):9869–9873, July, 2008.

    Article  MathSciNet  Google Scholar 

  8. S. Gurevich, R. Hadani, and N. Sochen, On some deterministic dictionaries supporting sparsity, Journal of Fourier Analysis and Applications, 14(5–6):859–876, December, 2008.

    Article  MATH  MathSciNet  Google Scholar 

  9. R. Gribonval and M. Nielsen, Sparse decompositions in unions of bases, IEEE Trans. on Information Theory, 49(12):3320–3325, 2003.

    Article  MathSciNet  Google Scholar 

  10. W. Heisenberg, The physical principles of the quantum theory, (C. Eckart and F.C. Hoyt, trans.), University of Chicago Press, Chicago, IL, 1930.

    Google Scholar 

  11. R.A. Horn C.R. Johnson, Matrix Analysis, New York: Cambridge University Press, 1985.

    MATH  Google Scholar 

  12. X. Huo, Sparse Image representation Via Combined Transforms, PhD thesis, Stanford, 1999.

    Google Scholar 

  13. J.B. Kruskal, Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics, Linear Algebra and its Applications, 18(2):95–138, 1977.

    Article  MATH  MathSciNet  Google Scholar 

  14. P.W.H. Lemmens and J.J. Seidel, Equiangular lines, Journal of Algebra, 24(3):494–512, 1973.

    Article  MATH  MathSciNet  Google Scholar 

  15. X. Liu and N.D. Sidiropoulos, Cramer-Rao lower bounds for low-rank decomposition of multidimensional arrays, IEEE Trans. on Signal Processing, 49(9):2074–2086, 2001.

    Article  MathSciNet  Google Scholar 

  16. B.K. Natarajan, Sparse approximate solutions to linear systems, SIAM Journal on Computing, 24:227–234, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  17. W.W. Peterson and E.J. Weldon, Jr., Error-Correcting Codes, 2nd edition, MIT Press: Cambridge, Mass., 1972.

    MATH  Google Scholar 

  18. A. Pinkus, N-Width in Approximation Theory, Springer, Berlin, 1985.

    Google Scholar 

  19. T. Strohmer and R.W. Heath, Grassmannian frames with applications to coding and communication, Applied and Computational Harmonic Analysis, 14:257–275, 2004.

    Article  MathSciNet  Google Scholar 

  20. J.A. Tropp, Greed is good: Algorithmic results for sparse approximation, IEEE Trans. On Information Theory, 50(10):2231–2242, October 2004.

    Article  MathSciNet  Google Scholar 

  21. J.A. Tropp, I.S. Dhillon, R.W. Heath Jr., and T. Strohmer, Designing structured tight frames via alternating projection, IEEE Trans. Info. Theory, 51(1):188–209, January 2005.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Elad .

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Elad, M. (2010). Uniqueness and Uncertainty. In: Sparse and Redundant Representations. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7011-4_2

Download citation

Publish with us

Policies and ethics