## 6 Parameters Initialization
Here, AUC stands for the area under the curve, and ROC denotes the receiver operating characteristic curve. We report this analysis in the following sections. ## 11 Confidence Intervals

We show, in Figure 8, the confidence intervals computed for face recognition experiments on the YaleB and AR databases, and fifteen scene categorization experiments on the Fifteen Scene Category database. We performed 10 experiments in each set of experiments for these databases and computed average classification accuracy along with standard deviation. As the number of experiments is 10 in each set of experiments, and 10 classification accuracy values cannot determine the true mean, we followed the t-test based on a 95% confidence level. Based on the 95% confidence level, we computed the confidence intervals for the methods in each set of experiments. The bar graphs of the means along with confidence intervals bars are shown in Figure 8. We explain the performance of the methods based on confidence intervals in the following sections. ### Confidence Intervals for Yaleb Dataset

Figure 8 presents the mean values along with error bars for the confidence intervals of the methods. The confidence interval for our approach lies at the highest location compared with the locations of the confidence intervals of other methods. Moreover, it does not overlap with any of the other intervals. The width of the confidence interval is also comparable to the intervals of the other methods. Our method predicts with a higher rate than others with a confidence level of 95%. In other words, it outperforms other approaches. ### Confidence Intervals for Ar Database

We performed two sets of experiments for each method. We computed confidence intervals for the set of experiments using 20 examples per class for training for each method. The confidence intervals computed for these methods are visually shown in Figure 8. The confidence interval mean value of our method is higher than the mean values of all other intervals. Though it partially overlaps with one of the intervals, its narrow range and higher position make it prominent for predicting higher true mean values within a small range with a 95% confidence level. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Accuracy (\%)** & **Test Time (ms)** & **Training Time (min)** \\ \hline JBDC [24] & 93.43 \(\pm\) 4.37 & 15.44 & 30.32 \\ Our Method & **95.00 \(\pm\) 1.75** & **4.98** & **22.00** \\ \hline \end{tabular}
\end{table} 
Table 6: Action recognition for action bank features (processed data) [58] of the UCF sports action database [59] with the five fivefold cross-validations experiment. ### Confidence Intervals for Fifteen Scene Category Database

We performed two sets of experiments for each method. One set of 10 experiments is based upon a random selection of 50 samples per class for training samples. We visually show the confidence intervals of the methods in Figure 8. It clearly shows the highest position of the confidence interval of our method. Moreover, the higher values of the mean lie within a narrow range with a 95% confidence level. Its extraordinary performance is very obvious. We also show the confidence intervals of the set of 10 experiments with 20 examples per class for a training set for each method. The confidence intervals computed for these methods are visually shown in Figure 8. The confidence interval's mean value of our method is higher than the mean values of all other intervals. Though the interval partially overlaps with one of the other intervals, comparatively, its higher position makes it distinctive for predicting higher true mean values with a 95% confidence level. ## 12 Auc-Roc Analysis

We show, in Figure 9, the ROC (receiver operating characteristic) curves of all the classes based upon One-vs-the-Rest (OvR) settings, considering one class as positive and all others as negative. The curves show the trend of true positive rate with false positive rate for different threshold values for all classes with positive-negative settings.