Sparse Linear Representation

Details Sparse Linear Representation Sparse Linear Representation

2009-05-13

arxiv.org/abs/0905.1990v2

Computer Science

Information Theory

5 pages, to appear in proc. IEEE ISIT, June 2009

Scientific paper

This paper studies the question of how well a signal can be reprsented by a sparse linear combination of reference signals from an overcomplete dictionary. When the dictionary size is exponential in the dimension of signal, then the exact characterization of the optimal distortion is given as a function of the dictionary size exponent and the number of reference signals for the linear representation. Roughly speaking, every signal is sparse if the dictionary size is exponentially large, no matter how small the exponent is. Furthermore, an iterative method similar to matching pursuit that successively finds the best reference signal at each stage gives asymptotically optimal representations. This method is essentially equivalent to successive refinement for multiple descriptions and provides a simple alternative proof of the successive refinability of white Gaussian sources.

Affiliated with

Also associated with

No associations

Rating

Sparse Linear Representation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sparse Linear Representation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparse Linear Representation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-128464