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Award ID contains: 1650449

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  1. ; ( , IEEE Transactions on Information Theory)
    null (Ed.)
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  2. ; ( , 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
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  3. ; ; ; ( , Sampling Theory and Applications (SampTA), 2017 International Conference on)
    Recent work has demonstrated the effectiveness of gradient descent for recovering low-rank matrices from random linear measurements in a globally convergent manner. However, their performance is highly sensitive in the presence of outliers that may take arbitrary values, which is common in practice. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against outliers, where the truncation is performed to rule out the contributions from samples that deviate significantly from the sample median. A restricted isometry property regarding the sample median is introduced to provide a theoretical footing of the proposed algorithm for the Gaussian orthogonal ensemble. Extensive numerical experiments are provided to validate the superior performance of the proposed algorithm. 
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