Research Article
Robust Matching Pursuit Extreme Learning Machines
Input: samples | Output: weight vector | Parameters setting: number of hidden nodes , regularization parameter and sparsity level . | Initialization: randomly initialize ELM parameters: input weights and biases in measurement matrix . | Set the index set , the residual , the iteration counter and . | for do | | Find a column of most correlated with the residual | | Augment the index set | | Solve the KRSLMP minimization problem by the following iterations | | | The solution is denoted as | Update residual | end for |
|