## 4 Experimental Evaluation
### Representation of Features with Different Numbers of Neurons
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(L\)} & \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**IA**} & \multicolumn{6}{c}{**TIC**} \\ \cline{2-13}  & & **CIFAR10** & **UMIst** & **MNIST** & **NORB** & **SCms** & **Caltech256** & **CIFAR10** & **UMIst** & **MNIST** & **NORB** & **SCms** & **Caltech256** \\ \hline
1500 & PISTA & — & 0.9965 & — & 1.0000 & 0.9874 & 0.9903 & — & 0.0297 & — & 9.81 \(\times\) 10\({}^{-1}\) & 0.0573 & 0.0476 \\  & G-PISTA & — & 0.9961 & — & 1.0000 & 0.9874 & 0.9903 & — & 0.0313 & — & 9.99 \(\times\) 10\({}^{-1}\) & 0.0573 & 0.0476 \\  & LC-PISTA & — & 0.9962 & — & 1.0000 & 0.9874 & 0.9903 & — & 0.0308 & — & 9.81 \(\times\) 10\({}^{-1}\) & 0.0573 & 0.0476 \\  & PISTA & — & 0.9973 & — & 1.0000 & 0.9903 & 0.924 & — & 0.0261 & — & 2.16 \(\times\) 10\({}^{-1}\) & 0.0505 & 0.0422 \\  & G-PISTA & — & 0.9989 & — & 1.0000 & 0.9903 & 0.9924 & — & 0.0279 & — & 2.15 \(\times\) 10\({}^{-1}\) & 0.0505 & 0.0422 \\  & LC-PISTA & — & 0.9970 & — & 1.0000 & 0.9903 & 0.9924 & — & 0.0273 & — & 2.15 \(\times\) 10\({}^{-1}\) & 0.0505 & 0.0422 \\ \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(L\)} & \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**MN**} & \multicolumn{6}{c}{**TIC**} \\ \cline{2-13}  & & **CIFAR10** & **UMIst** & **MNIST** & **NORB** & **SCms** & **Caltech256** & **CIFAR10** & **UMIst** & **MNIST** & **NORB** & **SCms** & **Caltech256** \\ \hline
100 & PISTA & — & 0.9965 & — & 1.0000 & 0.9874 & 0.9903 & — & 0.0297 & — & 9.81 \(\times\) 10\({}^{-1}\) & 0.0573 & 0.0476 \\  & G-PISTA & — & 0.9961 & — & 1.0000 & 0.9874 & 0.9903 & — & 0.0313 & — & 9.99 \(\times\) 10\({}^{-1}\) & 0.0573 & 0.0476 \\
200 & LC-PISTA & — & 0.9962 & — & 1.0000 & 0.9874 & 0.9903 & — & 0.0308 & — & 9.81 \(\times\) 10\({}^{-1}\) & 0.0573 & 0.0476 \\  & PISTA & — & 0.9973 & — & 1.0000 & 0.9903 & 0.924 & — & 0.0261 & — & 2.16 \(\times\) 10\({}^{-1}\) & 0.0505 & 0.0422 \\  & G-PISTA & — & 0.9989 & — & 1.0000 & 0.9903 & 0.9924 & — & 0.0279 & — & 2.15 \(\times\) 10\({}^{-1}\) & 0.0505 & 0.0422 \\  & LC-PISTA & — & 0.9970 & — & 1.0000 & 0.9903 & 0.9924 & — & 0.0273 & — & 2.16 \(\times\) 10\({}^{-1}\) & 0.0505 & 0.0422 \\ \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 7: MIN and MAX values achieved by PISTA, G-PISTA and LC-FSTA for training of the ELM-AE. The best tuning of the parameter \(\lambda\) in ELM-AE usually influences its generalization capacity. With the \(\lambda\) in Table 3 chosen using cross-validation, we show in Table 8 the performance of our method for the particular case \(L=2000\). The values of RMSE and the training time are the two indicators selected to compare the efficiency of FISTA, G-FISTA, and LC-FISTA in ELM-AE. The time shown in Table 8 is the execution time required to select the parameter \(\lambda\). Note that G-FISTA and LC-FISTA maintain the reconstruction capability of FISTA but perform better in terms of computational time. In addition, the values of RMSE in Tables 4 and 8 for the first and second scenarios are comparable measures. Therefore, the \(\lambda\) chosen in the first scenario and the additional parameters may be adequate in large-scale application problems since cross-validation is not required.