Sparse Signal Recovery

The problem of sparse signal recovery has recently received much attention with the development of compressed sensing and results providing insights into the robustness of L1 based recovery methods. The problem of computing sparse solutions to an underdetermined linear system of equations has a much longer history. Prof. Rao and his group have been involved in this area since 1992. His first work was in the context of biomagnetic imaging.

I.F. Gorodnitsky, B. D. Rao and J. George, "Source Localization in Magnetoencephalagraphy using an Iterative Weighted Minimum Norm Algorithm, IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Pages: 167-171, Oct. 1992

Recognizing the importance of sparsity and its role in signal processing, Prof. Bresler and Prof. Rao organized a special session on sparsity at ICASSP 1998 titled "SPEC-DSP: SIGNAL PROCESSING WITH SPARSENESS CONSTRAINT". This is one of the earliest full session, if not the first session, to be dedicated to the issue of sparsity. In this session, Prof. Rao's paper discusses the importance of sparsity in signal processing in his article titled:

B. D. Rao, "Signal Processing with the Sparseness Constraint," IEEE Acoustics, Speech and Signal Processing, Seattle, Washington, Vol.1, Pages: 369 - 372, May. 1998

Additional overview and tutorial presentations

Plenary at SPAWC 2009, June 21-24, Perugia, Italy: Sparse Signal Recovery: Theory, Applications and Algorithms

Tutorial at ICASSP 2010, March 14-19, Dallas, Texas: Sparse Signal Recovery: Theory, Applications and Algorithms

His group has been active in this area and a selected list of publications is provided below. Complete publications list can be found here.

Selected Publications:

    2013

  1. Y. Jin and B. D. Rao, "Support Recovery of Sparse Signals in the Presence of Multiple Measurement Vectors", IEEE Transactions on Information Theory, Volume: 59 , Issue: 5, Page(s): 3139 - 3157, May 2013
  2. Zhilin Zhang, Bhaskar D. Rao, "Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation", IEEE Trans. on Signal Processing, vol. 61, no 8, pp 2009-2015, April 2013
  3. Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao, "Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning", IEEE Trans. on Biomedical Engineering, vol. 60, no. 2, pp. 300 - 309, February 2013
  4. Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao, "Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware", IEEE Trans. on Biomedical Engineering, vol. 60, no. 1, pp. 221 - 224, January 2013
  5. 2011

  6. Yuzhe Jin, Young-Han Kim and B. D. Rao, "Limits on Support Recovery of Sparse Signals via Multiple Access Communication Techniques", IEEE Transactions on Information Theory, Vol. 57, No. 12, pages 7877-7892, December 2011
  7. Zhilin Zhang, Bhaskar D. Rao, "Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning", IEEE Journal of Selected Topics in Signal Processing, Special Issue on Adaptive Sparse Representation of Data and Applications in Signal and Image Processing, vol.5, no. 5, pp. 912-926, September 2011
  8. David Wipf, Bhaskar D. Rao and S. Nagarajan, "Latent Variable Bayesian Models for Promoting Sparsity", IEEE Transactions on Information Theory, pp. 6236-6255, September 2011
  9. Yuzhe Jin and Bhaskar D. Rao, "Multipass Lasso Algorithms for Sparse Signal Recovery", IEEE International Symposium on Information Theory, St. Petersburg, Russia, July 31, 2011
  10. 2010

  11. Y. Jin, B. D. Rao, "Algorithms for Robust Linear Regression by Exploiting the Connection to Sparse Signal Recovery", IEEE International Conference on Acoustics, Speech, and Signal Processing, Dallas, TX, March 2010
  12. 2007

  13. D. P. Wipf and B. D. Rao, "An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem", IEEE Transactions on Signal Processing, Vol. 55, Issue 7, Part 2, pages: 3704-3716, July 2007
  14. D. P. Wipf, R. R. Ramirez, J. A. Palmer, S. Makeig, and B. D. Rao, "Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization", Advances in Neural Information Processing Systems 19, B. Scholkopf, J. Platt, and T. Hoffman, editors, 2007
  15. 2006

  16. J. A. Palmer, D. P. Wipf, K. Kreutz-Delgado, and B.D. Rao, "Variational EM Algorithms for Non-Gaussian Latent Variable Models", Advances in Neural Information Processing Systems 18, MIT Press, Y. Weiss, B. Scholkopf, and J. Platt, editors, 2006
  17. 2005

  18. S. F. Cotter, B. D. Rao, K. Engan, and K. K-Delgado, "Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors", IEEE Transactions on Signal Processing, Vol. 53, Issue. 7, Pages: 2477 - 2488, July 2005. An early version of this work was published in the 1998 IEEE Digital Signal processing workshop and can be downloaded from here
  19. 2004

  20. D. P. Wipf, J. A. Palmer, and B. D. Rao, "Perspectives on Sparse Bayesian Learning", Neural Information Processing Systems, Vol. 16, December 2004
  21. D. P. Wipf and B. D. Rao, "Sparse Bayesian Learning for Basis Selection", IEEE Transactions on Signal Processing, Special Issue on Machine Learning Methods in Signal Processi, Vol. 52, Pages: 2153 - 2164, August 2004
  22. 2003

  23. B. D Rao, K. Engan, S.F. Cotter, J. Palmer, and K. K-Delgado, "Subset Selection in Noise Based on Diversity Measure Minimization", IEEE Transactions on Signal Processing, Vol. 51, Issue: 3, Pages: 760-770, March 2003
  24. K. K-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. W. Lee, and T. J. Sejnowski, "Dictionary Learning Algorithms for Sparse Representation", Neural Computation, Vol. 15, Pages: 349-396, February 2003
  25. 2002

  26. S. F. Cotter and B. D. Rao, "Sparse Channel Estimation Via Matching Pursuit with Application to Equalization", IEEE Transactions on Communications, Vol. 50, Issue 3, Pages: 374 - 377, March 2002
  27. 1999

  28. S. F. Cotter, J. Adler, B. D. Rao, K. K-Delgado, "Forward Sequential Algorithms for Best Basis Selection", Proceedings Vision, Image, and Signal Processing, Pages: 235-244, October 1999
  29. B. D. Rao and K. K-Delgado, "An Affine Scaling Methodology for Best Basis Selection", IEEE Transactions On Signal Processing, Vol. 47, Pages: 187-200, January 1999
  30. 1997

  31. I. F. Gorodnitsky and B. D. Rao, "Sparse Signal Reconstruction from Limited Data Using FOCUSS: A Re-Weighted Norm Minimization Algorithm", IEEE Transactions on Signal Processing, Vol. 45, Issue. 3, Pages: 600 - 616, March 1997
  32. 1995

  33. I. F. Gorodnitsky, J. George and B. D. Rao, "Neuromagnetic Source Imaging with FOCUSS: A Recursive Weighted Minimum Norm Algorithm", Electrocephalography and Clinical Neurophysiology 95, Pages: 231-251, January 1995

For the complete list of publications, click here.

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