Stephen J. Wright
- Fields of study
- Signal processing
The paper by Stephen J. Wright, Robert D. Nowak, and Mário A.T. Figueiredo on the reconstruction of sparse signals is considered to be one of the more significant recent advances in signal processing. Many signals and systems can be represented as a linear combination of elementary mathematical functions. The paper addresses the problem of selecting a small number of such functions—a subset of a large set of possible functions—so that the signal or system can be represented effectively as a linear combination of this subset. The algorithmic framework presented in the paper, which appeared in the July 2009 issue of the IEEE Transactions on Signal Processing (vol. 57, no. 7, pp. 2479–2493), unifies and extends previous iterative algorithms. Called “SpaRSA,” this powerful tool combines aspects of convex optimization, linear algebra, and dimensionality reduction to automatically determine near-optimal sparse approximations. The method has impacted signal processing applications in image processing, medical imaging, sampling, digital-to-analog conversion, wireless communications, radar, sonar, and machine learning. It has provided the foundation for further research activity in this increasingly important field.
Dr. Wright is a professor in the Computer Sciences Department at the University of Wisconsin, Madison, WI, USA.