Low Rank Tensor Completion With Poisson Observations

• Published in: IEEE transactions on pattern analysis and machine intelligence 2022-08, Vol.44 (8), p.4239-4251
• Main Authors: Zhang, Xiongjun, Ng, Michael K.
• Format: Article
• Language: English
• Subjects: Computational geometry; Computer vision; Convex functions; Convexity; Image processing; Information theory; low-rank tensor completion; Manifolds; Mathematical analysis; Matrix decomposition; Maximum likelihood estimate; Maximum likelihood estimates; Maximum likelihood estimation; Numerical models; Optimization models; Poisson distribution; Poisson observations; Tensors; transformed tensor nuclear norm; Upper bounds; Video; Videos
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2021.3059299

Poisson observations for videos are important models in video processing and computer vision. In this paper, we study the third-order tensor completion problem with Poisson observations. The main aim is to recover a tensor based on a small number of its Poisson observation entries. A existing matrix-based method may be applied to this problem via the matricized version of the tensor. However, this method does not leverage on the global low-rankness of a tensor and may be substantially suboptimal. Our approach is to consider the maximum likelihood estimate of the Poisson distribution, and utilize the Kullback-Leibler divergence for the data-fitting term to measure the observations and the underlying tensor. Moreover, we propose to employ a transformed tensor nuclear norm ball constraint and a bounded constraint of each entry, where the transformed tensor nuclear norm is used to get a lower transformed multi-rank tensor with suitable unitary transformation matrices. We show that the upper bound of the error of the estimator of the proposed model is less than that of the existing matrix-based method. Also an information theoretic lower error bound is established. An alternating direction method of multipliers is developed to solve the resulting convex optimization model. Extensive numerical experiments on synthetic data and real-world datasets are presented to demonstrate the effectiveness of our proposed model compared with existing tensor completion methods.