- There is time for dithering in a quantized world of reduced dimensionality!
- Quantized sub-Gaussian random matrices are still RIP!
- Quasi-isometric embeddings of vector sets with quantized sub-Gaussian projections
- Testing a Quasi-Isometric Quantized Embedding
- When Buffon’s needle problem meets the Johnson-Lindenstrauss Lemma
- 16,247 hits
Category Archives: Compressed Sensing
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
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
(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
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) . I recall that such a matrix is RIP if there exists a … Continue reading
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