Low-rank matrix factorization with missing elements has many applications in computer vision. However, the original model without taking any prior information, which is to minimize the total reconstruction error of all the observed matrix elements, sometimes provides a physically meaningless solution in some applications. In this paper, we propose a regularized low-rank factorization model for a matrix with missing elements, called Smooth Incomplete Matrix Factorization (SIMF), and exploit a novel image/vid...
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