Robust Regression Model Based on Low Rank Representation
摘要
The existing linear regression method cannot effectively deal with noise and outliers.To address the problem,this paper establishes the LR RRM model by combining Low Rank Representation(LRR) and robust regression methods.The LRR method is used to detect noise and outliers in the data in a supervised way.The clean part of data is recovered from the low dimensional subspace of the original data and is used for the classification of linear regression,so as to improve the regression performance.Experimental results on the Extend YaleB,AR,ORL and PIE face datasets show that compared with the standard linear regression model,the robust principal component analysis based linear regression model and the LRR linear regression model,the proposed model has better classification accuracy and robustness on the four original dataset and the dataset with random noise.