L_(2,1)-norm robust regularized extreme learning machine for regression using CCCP method

As a way of training a single hidden layer feedforward network(SLFN),extreme learning machine(ELM)is rapidly becoming popular due to its efficiency.However,ELM tends to overfitting,which makes the model sensitive to noise and outliers.To solve this problem,L_(2,1)-norm is introduced to ELM and an L_(2,1)-norm robust regularized ELM(L_(2,1)-RRELM)was proposed.L_(2,1)-RRELM gives constant penalties to outliers to reduce their adverse effects by replacing least square loss function with a non-convex loss function.In light of the non-convex feature of L_(2,1)-RRELM,the concave-convex procedure(CCCP)is applied to solve its model.The convergence of L_(2,1)-RRELM is also given to show its robustness.In order to further verify the effectiveness of L_(2,1)-RRELM,it is compared with the three popular extreme learning algorithms based on the artificial dataset and University of California Irvine(UCI)datasets.And each algorithm in different noise environments is tested with two evaluation criterions root mean square error(RMSE)and fitness.The results of the simulation indicate that L_(2,1)-RRELM has smaller RMSE and greater fitness under different noise settings.Numerical analysis shows that L_(2,1)-RRELM has better generalization performance,stronger robustness,and higher anti-noise ability and fitness.