Error estimation theorems of two low rank matrix restoration optimization models
In recent years,the problem of low rank matrix restoration has gradually attracted people's attention.Sim-ilar to vector sparse restoration,the sufficient condition is that the measurement matrix needs to meet the Restricted isometric property,and the sufficient condition of low rank matrix restoration is that a linear mapping needs to meet the Restricted isometric property.The restoration of low rank matrix is divided into two restoration models with noise and no noise.The restored results need different restricted isometric constant bounds to ensure.In this paper,the er-ror bound estimation theorems of the two optimization models are proved,and two different restricted isometric con-stant bounds are obtained.
restricted isometric property of linear mappinglow rank matrix recoverycompressed sensingfrobenius norm