A universal rank approximation method for matrix completion

Matrix completion is critical in a wide range of scientific and engineering applications, such as image restoration and recommendation systems. This topic is commonly expressed as a low-rank matrix optimization framework. In this paper, a universal and effective rank approximation method for matrix completion (RAMC) is provided. Fundamental to this strategy is developing a general function that meets specific conditions in order to directly approach the rank function and subsequently utilizing it to build a RAMC model. The major goal is to investigate a more accurate estimate of the rank function, allowing for more effective acquisition of the low-rank structure of incomplete data. Further, the RAMC model is easily implemented by a viable iterative method that may be successfully used to matrix completion tasks. Extensive experiments using the synthetic data and natural images reveal the excellent applicability of RAMC over the existing methods.