- 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
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Category Archives: General
I’m glad to announce here a new work made in collaboration with Valerio Cambareri (UCL, Belgium) on quantized embeddings of low-complexity vectors, such as the set of sparse (or compressible) signals in a certain basis/dictionary, the set of low-rank matrices … Continue reading
I have always been intrigued by the fact that, in Compressed Sensing (CS), beyond Gaussian random matrices, a couple of other unstructured random matrices respecting, with high probability (whp), the Restricted Isometry Property (RIP) look like “quantized” version of the … 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
Last Thursday after an email discussion with Thomas Arildsen, I was thinking again to the nice embedding properties discovered by Y. Plan and R. Vershynin in the context of 1-bit compressed sensing (CS) . I was wondering if these could … 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
I have just found this “new” (well 150 years old actually) tomographical method… for measuring the magnetic field of our own galaxy “New all-sky map shows the magnetic fields of the Milky Way with the highest precision“by Niels Oppermann et … Continue reading