Research Activities > Programs > Sparse Data Representation

Sparse Data Representation: The Role of Redundancy in Data Processing

April 11 - April 15, 2005   AND   May 9 - May 13, 2005

CSIC Building (#406), Seminar Room 4122.

Program Overview
Workshop 1
April 11 - April 15 2005
Oversampling and Coarse Quantization for Signals
Workshop 2
May 9 - May 13 2005
Sparse Representation in Redundant Systems

Quick Navigator
Scientific Content Funding Information for Participants
Contact Poster Photos
Suggested Activities   Acknowledgement



The last decade has seen a dramatic increase in computational power and sensor ubiquity, as well as an ever increasing demand for finer resolution in both scientific and geometric modeling. This has led to the creation of enormously large data sets with exquisite detail. However, these data sets will be useful only if we can process them efficiently, whether it be for storage, transmission, visual display, fast on-line graphical query, correlation, or registration against data from other modalities.

Raw data sets are typically inaccessible and need to be transformed to more efficient representations for further processing. Several competing issues emerge. Sparsity is essential for efficient transmission, storage, and computation. Multiscale representations are critical to extract features at desired scales. Implementation in silicon leads to new issues of robustness in the face of computational error and imprecise circuit implementation.

An emerging technology to address these issues utilizes redundant representations. High oversampling followed by coarse quantization is the preferred method for analog to digital conversion of signals. Sparse representation of images using redundant families of waveforms is effectively utilized in feature extraction and denoising. These redundant families can be frames, dictionaries, or libraries of bases.

On the other hand, there is, at present, no compelling theory to explain the advantages of redundancy in image and signal processing. This program will convene leading experts from data representation into two workshops to describe the current understanding of the benefits of redundancy and to set forward a program for further research. These experts will come from diverse areas such as applied mathematics, statistics, computer science, engineering, and circuit design.

WORKSHOP 1 (April 11 - April 15 2005)

Oversampling and Coarse Quantization for Signals

WORKSHOP 2 (May 9 - May 13 2005)

Sparse Representation in Redundant Systems

Back to Top


Partial funding is provided by the University of South Carolina Industrial Mathematics Institute (IMI)
and by the Office of Naval Research (ONR).


A limited amount of funding for participants at all levels is available, especially for researchers in the early stages of their career who want to attend the full program.

Back to Top


CSCAMM Visitor Guide:


Center for Scientific Computation And Mathematical Modeling (CSCAMM)
Computer Science Instructional Center (Building #406)
University of Maryland, College Park
College Park, MD 20742-3289


Web: /programs/sdr05

Back to Top