Low-Rank Matrix Completion with Poisson Observations via Nuclear Norm and Total Variation Constraints
Duo Qiu,
Michael K. Ng null,
Xiongjun Zhang
Abstract: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 underlyin… Show more
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