One-bit quantization with time-varying sampling thresholds (also known as random
dithering) has recently found significant utilization potential in statistical signal processing …

The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse
vector from binary-valued measurements, each indicating the sign of a linear function of the …

Compressed sensing has been a very successful high-dimensional signal acquisition and
recovery technique that relies on linear operations. However, the actual measurements of …

One-bit compressive sensing gains its popularity in signal processing and communications
due to its low storage costs and low hardware complexity. However, it has been a …

One-bit compressed sensing (1bCS) is an extreme-quantized signal acquisition method that
has been intermittently studied in the past decade. In 1bCS, linear samples of a high …

Recovery of support of a sparse vector from simple measurements is a widely studied
problem, considered under the frameworks of compressed sensing, 1-bit compressed …

In the problem of learning a mixture of linear classifiers, the aim is to learn a collection of
hyperplanes from a sequence of binary responses. Each response is a result of querying …

In 1-bit compressed sensing, the aim is to estimate ak-sparse unit vector x∈ Sn-1 within an
e error (in ℓ2) from minimal number of linear measurements that are quantized to just their …

Conventional sparse phase retrieval schemes can recover sparse signals from the
magnitude of linear measurements only up to a global phase ambiguity. This work proposes …

One-bit compressed sensing (1bCS) is an extremely quantized signal acquisition method
that has been proposed and studied rigorously in the past decade. In 1bCS, linear samples …