Robust Low Rank Matrix Recovery And Application Of Sparse Logistic Regression Model

Sparse representation of data is widely used in modern science and engineering due to its unique advantages.Inspired by the recent researches on compressed perception and sparse restoration,this paper summarizes their research status from two aspects: Low Rank Matrix Restoration and sparse logistic regression model.For the low rank matrix restoration problem,the low rank approximation properties of random operators and the restricted equidistant properties are discussed,and the low rank approximation properties are associated with the related random operators,according to the previous results and the actual problems of operators,a new matrix restoration problem is proposed.The Lasso regression model and Danselzig model are discussed and proved,finally,the validity of the model and its applicability to more general operators are proved.Data classification is applied in many aspects of life,in order to improve the precision and accuracy of sparse data classification,this paper proposes a link neural network model based on sparse logistic regression,so to build a reliable classification model.Taking two types of data as research object,data is preprocessed first,and then data features are extracted to classify them.The research results show that the classification model proposed in this paper can not only be applied to sparse data,but the accuracy is improved compared with the results of the neural network model.Accuracy of handwriting has increased from 90.1% to 94.86%,and accuracy of sound classification has increased from 70.3% to 74.4%,which proves that the model is effective.