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Matrix completion from quantized samples via generalized sparse Bayesian learning
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AI
DOI：10.1016/j.dsp.2025.105575.png)
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Abstract
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• A generalized sparse Bayesian learning approach is proposed for low-rank matrix completion from coarsely quantized data. • The method is extended to 2D line spectral estimation via incorporation with the MUSIC algorithm. • Numerical simulation and real data experiment demonstrate the effectiveness of the proposed approach.
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