Year: 2019
Type: article
Abstract: We consider the problem of jointly recovering the vector band the matrix C from noisy measurements Y = A(b)C + W,where A(·) is a known affine linear function of b(i.e., A(b) = A0+ Σi=1QbiAiwith known matrices Ai). This problem has applications in matrix completion, robust PCA, dictionary learning, self-calibration, blind deconvolution, joint-channel/symbol estimation, compressive sensing with matrix uncertainty, and many other tasks. To solve this bilinear recovery problem, we propose the Bilinear Adaptive Vector Approximate Message Passing (VAMP) algorithm. We demonstrate numerically that the proposed approach is competitive with other state-of-the-art approaches to bilinear recovery, including lifted VAMP and Bilinear Generalized Approximate Message Passing.
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: NeTS: Medium: Massive Mobile Broadband Communications with Millimeter Wave Picocellular Networks, CAREER: Structured Nonlinear Estimation via Message Passing: Theory and Applications, G3557183362, CIF: Medium: Collaborative Research: Scalable Learning of Nonlinear Models in Large Neural Populations, Approximate Message Passing Algorithms and Networks, CIF: Small: Approximate Message Passing for Systems with Linear Mixing and Randomization, EARS: Spectrum and Infrastructure Sharing in Millimeter Wave Cellular Networks
OpenAlex ID: W2890334221