A simple proof that random matrices are democratic

Mathematics – Numerical Analysis

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

The recently introduced theory of compressive sensing (CS) enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be significantly smaller than the ambient dimension of the signal and yet preserve the significant signal information. Interestingly, it can be shown that random measurement schemes provide a near-optimal encoding in terms of the required number of measurements. In this report, we explore another relatively unexplored, though often alluded to, advantage of using random matrices to acquire CS measurements. Specifically, we show that random matrices are democractic, meaning that each measurement carries roughly the same amount of signal information. We demonstrate that by slightly increasing the number of measurements, the system is robust to the loss of a small number of arbitrary measurements. In addition, we draw connections to oversampling and demonstrate stability from the loss of significantly more measurements.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

A simple proof that random matrices are democratic 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 A simple proof that random matrices are democratic, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A simple proof that random matrices are democratic will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-619488

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.