Mixed linear matrix completion model based on auxiliary information

The matrix completion technology has been applied in many fields in recent years. Using existing auxiliary information to perform matrix completion to improve the accuracy of the completion has attracted attention. A matrix completion model is proposed, which mixes bilinear and unilateral linear relationships, considering the correlation between row information and column information and their respective characteristics, so that the mixed linear model can approximate the original matrix entries. At the same time, the convergence of using the ADMM algorithm to solve the convex optimization problem is proved, and makes two sets of experiments with synthetic datasets and real datasets, which proves that the proposed method is more effective compared with the existing model using auxiliary information, whose error under RMSE evaluation standard was reduced by more than 25% than other methods.