Phase transitions in higher dimension, pt. 2000

My tests at an ambient dimension of \(N=2000\) are nearly finished for signals distributed Normally; Rademacher is yet to simulation, but that will have to wait after my trip to Bretagne.
Previously, here are my tests at a dimensionality of \(N=400\), and here at \(N=1000\). I don’t really see any game changers here. For some reason, we see CompCMP flattens out at higher indeterminaces, but beats SL0 and PrOMP at midlevel indeterminaces, but these could be due to me only averaging the results of 25 signals at each pair of sparsity and indeterminacy. (Again, the criteria here for successful recovery is exact support recovery, i.e., no missed detections and no false alarms.)

Coming soon! Our finished Asilomar 2012 paper: B. L. Sturm, M. G. Christensen, and R. Gribonval, “Cyclic Pure Greedy Algorithms for Recovering Compressively Sampled Sparse Signals”.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s