Approximate Message Passing with Unitary Transformation for Robust Bilinear Recovery

by   Zhengdao Yuan, et al.

Recently, several promising approximate message passing (AMP) based algorithms have been developed for bilinear recovery with model Y=∑_k=1^K b_k A_k C +W, where {b_k} and C are jointly recovered with known A_k from the noisy measurements Y. The bilinear recover problem has many applications such as dictionary learning, self-calibration, compressive sensing with matrix uncertainty, etc. In this work, we propose a new bilinear recovery algorithm based on AMP with unitary transformation. It is shown that, compared to the state-of-the-art message passing based algorithms, the proposed algorithm is much more robust and faster, leading to remarkably better performance.


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