TY - JOUR T1 - Low-Rank Matrix Completion with Poisson Observations via Nuclear Norm and Total Variation Constraints AU - Qiu , Duo AU - K. Ng , Michael AU - Zhang , Xiongjun JO - Journal of Computational Mathematics VL - 6 SP - 1427 EP - 1451 PY - 2024 DA - 2024/11 SN - 42 DO - http://doi.org/10.4208/jcm.2309-m2023-0041 UR - https://global-sci.org/intro/article_detail/jcm/23503.html KW - Low-rank matrix completion, Nuclear norm, Total variation, Poisson observations. AB -
In this paper, we study the low-rank matrix completion problem with Poisson observations, where only partial entries are available and the observations are in the presence of Poisson noise. We propose a novel model composed of the Kullback-Leibler (KL) divergence by using the maximum likelihood estimation of Poisson noise, and total variation (TV) and nuclear norm constraints. Here the nuclear norm and TV constraints are utilized to explore the approximate low-rankness and piecewise smoothness of the underlying matrix, respectively. The advantage of these two constraints in the proposed model is that the low-rankness and piecewise smoothness of the underlying matrix can be exploited simultaneously, and they can be regularized for many real-world image data. An upper error bound of the estimator of the proposed model is established with high probability, which is not larger than that of only TV or nuclear norm constraint. To the best of our knowledge, this is the first work to utilize both low-rank and TV constraints with theoretical error bounds for matrix completion under Poisson observations. Extensive numerical examples on both synthetic data and real-world images are reported to corroborate the superiority of the proposed approach.