In compressed sensing we seek to gain information about vector x ∈ RN from d << N nonadaptive linear measurements. Candes, Donoho, Tao et. al. ( see e.g. [2, 4, 8]) propos...
In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training...
Tara N. Sainath, Avishy Carmi, Dimitri Kanevsky, B...
In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain this knowledge by estimating the power spe...
Dennis Sundman, Saikat Chatterjee, Mikael Skoglund
We initiate the study of sparse recovery problems under the Earth-Mover Distance (EMD). Specifically, we design a distribution over m × n matrices A such that for any x, given A...
The literature on compressed sensing has focused almost entirely on settings where the signal is noiseless and the measurements are contaminated by noise. In practice, however, th...