
Recent Posts
 There is time for dithering in a quantized world of reduced dimensionality!
 Quantized subGaussian random matrices are still RIP!
 Quasiisometric embeddings of vector sets with quantized subGaussian projections
 Testing a QuasiIsometric Quantized Embedding
 When Buffon’s needle problem meets the JohnsonLindenstrauss Lemma
Archives
Blogroll
Related Links and Blogs
Blog Stats
 17,996 hits
Category Archives: Compressed Sensing
Quasiisometric embeddings of vector sets with quantized subGaussian projections
Last January, I was honored to be invited in RWTH Aachen University by Holger Rauhut and Sjoerd Dirksen to give a talk on the general topic of quantized compressed sensing. In particular, I decided to focus my presentation on the … Continue reading
Testing a QuasiIsometric Quantized Embedding
It took me a certain time to do it. Here is at least a first attempt to test numerically the validity of some of the results I obtained in “A Quantized Johnson Lindenstrauss Lemma: The Finding of Buffon’s Needle” (arXiv) I … Continue reading
When Buffon’s needle problem meets the JohnsonLindenstrauss Lemma
(This post is related to a paper entitled “A Quantized Johnson Lindenstrauss Lemma: The Finding of Buffon’s Needle” (arxiv, pdf) that I have recently submitted for publication.) Last July, I read the biography of Paul Erdős written by Paul Hoffman … Continue reading
Posted in Compressed Sensing, General, Johnson Lindenstrauss
1 Comment
A useless nonRIP Gaussian matrix
Recently, for some unrelated reasons, I discovered that it is actually very easy to generate a Gaussian matrix that does not respect the restricted isometry property (RIP) [1]. I recall that such a matrix is RIP if there exists a … Continue reading
Posted in Compressed Sensing, General
Leave a comment
Some comments on Noiselets
Recently, a friend of mine asked me few questions about Noiselets for Compressed Sensing applications, i.e., in order to create efficient sensing matrices incoherent with signal which are sparse in the Haar/Daubechies wavelet basis. It seems that some of the … Continue reading
Posted in Compressed Sensing
9 Comments
SPGL1 and TV minimization ?
Recently, I was using the SPGL1 toolbox to recover some “compressed sensed” images. As a reminder, SPGL1 implements the method described in “Probing the Pareto Frontier for basis pursuit solutions” of Michael P. Friedlander and Ewout van den Berg. It … Continue reading
Posted in Compressed Sensing
23 Comments