Optimization of Data Matrix Completion by Symmetric Weighting Algorithm

Abstract:

Data set matrix is missing in data analysis, data elements can be completed by data matrix, and the efficient completion data matrix algorithm can be optimized and improved from the aspects of algorithm accuracy. A Symmetric Weighting (SW) algorithm is proposed. Firstly, according to the general matrix completion model, the regularization method is used to complete the low-rank matrix decomposition. Secondly, a new matrix completion model and a regularization weighting function are obtained by weighting the decomposed matrix factors with a common symmetric matrix. Finally, using block coordinate descent and alternate least square optimization algorithm, the optimal solution of the objective function is obtained iteratively, and the optimal completion matrix of data completion is obtained. Compared with APALM, IRSVF and IRNN, the symmetric weighting algorithm has better improvement in the precision and convergence speed of data matrix completion.