Low-rank Matrix Recovery Model Based on Non-negative Matrix Factorization
XU Mengke
XU Daoyun
WEI Mingjun
Abstract：To overcome the shortage of large-scale nuclear matrix singular value decomposition existing in low-rank matrix re?covery model,the paper proposed low-rank matrix recovery model based on non-negative matrix factorization. Non-negative matrix factorization(NMF) applied to the low-rank matrix,which could quickly deal with the problem of the decomposition matrix of low-rank and avoid large-scale nuclear matrix singular value decomposition. Then the algorithm used alternarting directions method of multipliers(ADMM). ADMM divided the global problem into partial sub-problems. Each sub-problem used Lagrange multipliers to solve low rank matrix and sparse matrix. Experimental results in ORL,AL_Gore and Windows databases showed that low-rank re?covery model based NMF has higher recognition rate,better reduction rank and lower the complexity of the algorithm than other tra?ditional low-rank recovery model.
Keywords：non-negative matrix factorization(NMF)low-rank matrix recoveryalternating directions method of multipli?erssingular value decomposition(SVD)image recognition
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1019-1024 )