TY - JOUR
T1 - Bilinear Recovery Using Adaptive Vector-AMP
AU - Sarkar, Subrata
AU - Fletcher, Alyson K.
AU - Rangan, Sundeep
AU - Schniter, Philip
N1 - Funding Information:
The work of S. Sarkar and P. Schniter was supported by the National Science Foundation under Grant 1716388. The work of A. K. Fletcher was supported in part by the National Science Foundation under Grant 1738285 and Grant 1738286, and in part by the Office of Naval Research under Grant N00014-15-1-2677. The work of S. Rangan was supported in part by the National Science Foundation under Grant 1116589, Grant 1302336, and Grant 1547332, and in part by the industrial affiliates of NYU WIRELESS.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - We consider the problem of jointly recovering the vector b and 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 + ∑i=1Q biAi with 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.
AB - We consider the problem of jointly recovering the vector b and 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 + ∑i=1Q biAi with 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.
KW - Approximate message passing
KW - computed tomography
KW - dictionary learning
KW - expectation maximization
KW - expectation propagation
KW - self-calibration
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U2 - 10.1109/TSP.2019.2916100
DO - 10.1109/TSP.2019.2916100
M3 - Article
AN - SCOPUS:85066625879
SN - 1053-587X
VL - 67
SP - 3383
EP - 3396
JO - IRE Transactions on Audio
JF - IRE Transactions on Audio
IS - 13
M1 - 8712432
ER -