## 3 Evaluation
### Experimental Setup
#### 3.2.2 Classification Setting
\(D^{\text{Tr}}\) is the original training set (without transformation) and \(\{D^{\text{Tr}}\), \(D^{\text{Tr}}\}\) is the original training \(D^{\text{Tr}}\) set augmented with the transformed (using our model) training set \(D^{\text{Tr}}\). \(\{Z_{tr},\,Z_{tr}\}\) is the features extracted from the autoencoder of the original training set \(D^{\text{Tr}}\) augmented with the extracted features of the transformed training set \(D^{\text{Tr}}\), respectively. We also compared our MSTAR three-target classification accuracy result (98.06%) with other research done on classification accuracy enhancement on the same dataset. We compared with some conventional methods that do not employ data augmentation such as [85] that uses a joint sparse representation (JRS) model, [86] which makes use of phase and amplitude as well as image data, a sparse representation classifier, and Riemannian manifolds, Euclidean distance restricted autoencoder [87], and a hierarchical recognition system that is based on a constrained restricted Boltzmann machine (RBM) [88]. In addition, we included a method that uses data augmentation (deep learning method based on visual cortical system [89]). The comparison results presented in Table 9 show that the LSVM using our model achieves a comparable accuracy to other research results; also, it is worth noting the effect of data augmentation on classification accuracy such as in [89] where using data augmentation enhances the classification accuracy by 4.6%. Besides the quantitative results that demonstrate our model's ability to improve the compactness and separability properties, we show qualitative results through visualizing the mapped features in the feature space using t-SNE [90]. Figure 6 depicts the t-SNE of the plain test set vs. the mapped test set for the five datasets. One can observe that the classes are more separable and compact, especially overlapped classes as, for example, shown by the digits 4 and 9 in the MNIST dataset (highlighted in red circles). \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Classification Accuracy** \\ \hline [PERSON] et al. [77] & 99.40\% \\ Proposed Method + SESSC & **99.23\%** \\ Proposed Method + LSVM & **98.83\%** \\ [PERSON] et al. [78] & 98.80\% \\ [PERSON] et al. [79] & 98.36\% \\ [PERSON] et al. [80] & 98.19\% \\ [PERSON] et al. [81] & 97.88\% \\ [PERSON] et al. [82] & 97.30\% \\ [PERSON] et al. [83] & 97.10\% \\ [PERSON] et al. [84] & 73.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The WDBC dataset comparison results of the proposed method with other methods. The results are sorted in descending order. Bolded values are the results obtained from this study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Classification Accuracy** \\ \hline Proposed Method + LSVM & **98.06\%** \\ [PERSON] et al. [89] (With Data Augmentation) & 96.70\% \\ [PERSON] et al. [86] & 96.10\% \\ [PERSON] et al. [88] & 95.31\% \\ [PERSON] et al. [87] & 94.14\% \\ [PERSON] et al. [85] & 93.20\% \\ [PERSON] et al. [89] (Without Data Augmentation) & 92.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The MSTAR three-target dataset comparison results of the proposed method with other methods. The results are sorted in descending order. Bolded value is the result of this study. Figure 6: _Cont._