Date

Aug 28, 2018

Location

Alan Turing Institute (London, UK)

(invited by H. Tyagi and M. Cucuringu)

**Abstract**: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model. While the resolution of this quantization clearly impacts the quality of signal reconstruction, there even exist incompatible combinations of quantization functions and sensing matrices that proscribe arbitrarily low reconstruction error when the number of measurements increases.

In this introductory talk, we will see that a large class of random matrix constructions, i.e., known to respect the restricted isometry property (RIP) in the compressive sensing literature, can be made “compatible” with a simple scalar and uniform quantizer (e.g., a rescaled rounding operation). This compatibility is simply ensured by the addition of a uniform random vector, or random “dithering”, to the compressive signal measurements before quantization.

In this context, we will first study how quantized, dithered random projections of “low-complexity” signals is actually an efficient dimensionality reduction technique that preserves the distances of low-complexity signals up to some controllable additive and multiplicative distortions. Second, the compatibility of RIP sensing matrices with the dithered quantization process will be demonstrated by the existence of (at least) one signal reconstruction method, the projected back projection (PBP), which achieves low reconstruction error, decaying when the number of measurements increases. Finally, by leveraging the quasi-isometry property reached by quantized, dithered random embeddings, we will show how basic signal classification (or clustering) can be realized from their QCS observations, i.e., without a reconstruction step. Here also the complexity, or intrinsic dimension, of the observed signals drives the final classification accuracy.