## 3 Evaluation
### Experimental Setup
#### 3.2.2 Classification Setting
 } & K-Means++ & \(D^{\text{Tr}}\) & 41.216 s & 30,290,316 & 0.505 & 0.536 & 0.520 & 0.338 & 0.505 & 0.151 \\  & \(D^{\text{Tr}}\) & **24.270 s** & **7,065,023** & **0.705** & **0.707** & **0.706** & **0.636** & **0.705** & **0.505** \\  & Random & \(D^{\text{Tr}}\) & 44.563 s & 30,243,539 & 0.493 & 0.518 & 0.505 & 0.348 & 0.493 & 0.137 \\  & PCA-Based & \(D^{\text{Tr}}\) & **38.562 s** & **7,067,789** & **0.693** & **0.712** & **0.702** & **0.607** & **0.693** & **0.510** \\  & PCA-Based & \(D^{\text{Tr}}\) & 10.170 s & 30,820,962 & 0.488 & 0.503 & 0.495 & 0.344 & 0.488 & 0.131 \\  & \(D^{\text{Tr}}\) & **8.454 s** & **8,803,305** & **0.675** & **0.690** & **0.682** & **0.587** & **0.675** & **0.479** \\ \hline \multirow{7}{*}{USPS} & K-Means++ & \(D^{\text{Tr}}\) & 1.636 s & 1,449,834 & 0.576 & 0.583 & 0.580 & 0.473 & 0.575 & 0.117 \\  & \(D^{\text{Tr}}\) & **1.038 s** & **333,792** & **0.847** & **0.848** & **0.848** & **0.863** & **0.847** & **0.615** \\  & Random & \(D^{\text{Tr}}\) & 1.328 s & 1,456,828 & 0.547 & 0.569 & 0.558 & 0.445 & 0.546 & 0.118 \\  & PCA-Based & \(D^{\text{Tr}}\) & **0.889 s** & **333,788** & **0.847** & **0.847** & **0.847** & **0.863** & **0.847** & **0.571** \\  & PCA-Based & \(D^{\text{Tr}}\) & 0.478 s & 1,449,824 & 0.577 & 0.584 & 0.580 & 0.474 & 0.576 & 0.119 \\  & \(D^{\text{Tr}}\) & **0.197 s** & **33,793** & **0.846** & **0.847** & **0.847** & **0.863** & **0.846** & **0.621** \\ \hline \multirow{7}{*}{MSTAR} & K-Means++ & \(D^{\text{Tr}}\) & 3.305 s & 9,242,633 & 0.012 & 0.016 & 0.014 & 0.009 & 0.009 & 0.015 \\  & \(D^{\text{Tr}}\) & **2.174 s** & **4,280,437** & **0.524** & **0.528** & **0.526** & **0.526** & **0.522** & **0.388** \\  & \(D^{\text{Tr}}\) & 2.142 s & 9,232,361 & 0.017 & 0.019 & 0.018 & 0.009 & 0.014 & 0.024 \\  & \(D^{\text{Tr}}\) & **1.589 s** & **4,280,491** & **0.522** & **0.527** & **0.524** & **0.523** & **0.520** & **0.390** \\  & PCA-Based & \(D^{\text{Tr}}\) & 0.835 s & 9,245,153 & 0.022 & 0.023 & 0.023 & 0.019 & 0.019 & 0.019 \\  & \(D^{\text{Tr}}\) & **0.590 s** & **4,280,491** & **0.522** & **0.527** & **0.524** & **0.523** & **0.520** & **0.371** \\ \hline \multirow{7}{*}{WDBC} & K-Means++ & \(D^{\text{Tr}}\) & 0.013 s & 3454 & 0.668 & 0.689 & 0.678 & 0.777 & 0.666 & 0.391 \\  & \(D^{\text{Tr}}\) & **0.010 s** & **958** & **0.904** & **0.904** & **0.904** & **0.953** & **0.904** & **0.873** \\ \cline{1-1}  & Random & \(D^{\text{Tr}}\) & 0.010 s & 3454 & 0.668 & 0.689 & 0.678 & 0.777 & 0.666 & 0.391 \\ \cline{1-1}  & PCA-Based & \(D^{\text{Tr}}\) & **0.008 s** & **958** & **0.904** & **0.904** & **0.904** & **0.953** & **0.904** & **0.873** \\ \hline \multirow{7}{*}{WNIST} & SESSC & \(D^{\text{Tr}}\) & 1.491 s & - & 0.871 & 0.867 & 0.869 & 0.930 & 0.868 & 0.286 \\ \cline{1-1}  & \(D^{\text{Tr}}\) & **1.  
classifiers were run for the five datasets and results are listed in Table 4 and illustrated in Figure 4. From the results, one can observe that the accuracy of both the neural network and the linear SVM classifiers are enhanced in nearly all the datasets. Also, we can see that the LSVM classifier's accuracies with our model are on par with the NN classifier which indicates that the linear separability is enhanced because of our model. We can observe that the SVM classifier outperforms the NN classifier on the MSTAR dataset in both settings, with and without our model. A plausible explanation for why the NN classifier performs poorly on such a dataset is that NN classifiers suffer when the input data dimensionality is high. This drawback of NN classifiers is relatively remedied when the number of training samples is adequately large relative to the number of parameters preventing the plausible overfitting [76]. This is unfortunately not available in our case of small datasets, especially the MSTAR dataset. On the other hand, SVM training requires a much smaller number of data samples as they rely on the support vectors which is a very small subset of the training samples.