Approximate Message Passing with Unitary Transformation for Robust Bilinear Recovery
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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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