Algorithm 2: EMO-ELM for sparse feature learning

Input: $\mathbf{X}$, $\rho$, L    Output: Learned feature 1 Optimize Equation (23) according to Algorithm 1, and obtain the Pareto optimal solution set; 2 Select ${\mathit{\alpha }}_F$ from the obtained Pareto optimal solution set according to selection criteria; 3 Regenerate ${\mathbf{W}}_F$ and ${\mathbf{b}}_F$; 4 Extract features according to Equation (24); 5 Return extracted features $\stackrel{\sim }{\mathbf{X}}$;