# Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
## 4 Experiments
### SRbPLRC for Large-Size Video Recognition  
On the Honda database, the biggest video sequence contains 618 images. On the Mobo database, the biggest video sequence includes 340 images, while on the YouTube Celebrities database, the biggest video sequence includes 349 frames. Hence, we will perform large-size video recognition experiments on these databases. Figure 4 illustrates the recognition rates of 10 random experiments using the DLRC and PLRC methods. The videos were from the Honda and Mobo databases and contained varying numbers of frames. In the experiments, PLRC-III and PLRC-IV are the combinations of PLRC and the S3 and S4 unrelated set construction strategies. From the figure, it can clearly be observed that the DLRC, PLRC-I, and PLRC-II methods only work well on small videos (when there are fewer than 150 frames in each video). Specifically, on the Honda database, the performance of the DLRC, PLRC-I, and PLRC-II methods is significantly reduced when the number of frames is larger than 150. For the Mobo database, the demarcation point for the DLRC, PLRC-I, and PLRC-II methods is also 150 frames. We also noticed that the PLRC-III and PLRC-IV methods perform better than the DLRC, PLRC-I, and PLRC-II methods. In subsequent experiments, the Honda, Mobo and YouTube Celebrities databases, with all their frames, were used as the large-sized videos. The average classification accuracies of the DSRbPLRC, APSRbPLRC, and AAPSRbPLRC algorithms are shown in Figure 5.  
Figure 4: The experimental results of DLRC and PLRC methods from two popular databases, with varying numbers of frames. Figure 5 lists a quantitative evaluation (i.e., the average classification accuracy) of the DSRbPLRC, APSRbPLRC, and AAPSRbPLRC algorithms for large-size videos. It can be observed that our proposed method outperforms PLRC in all cases, which indicates that our SRbPLRC framework is effective, i.e., the DSRbPLRC, APSRbPLRC, and AAPSRbPLRC algorithms can overcome the limitations of PLRC to some extent. We also found that the DSRbPLRC and APSRbPLRC algorithms achieve similar performances, while the AAPSRbPLRC algorithm achieves a better performance, which indicates that the attention mechanism could find more discriminative anchor points. From Figure 5, it can also be seen that the unrelated S3 and S4 set -construction strategies outperform the other unrelated subspace-construction strategies. These quantitative and qualitative comparisons confirm the applicability of the proposed method for large-size video recognition. Finally, Table 1 enumerates the performances of the different methods on the different databases when using all frames. The results of DSRbPLRC, APSRbPLRC and AAPSRbPLRC are the best classification accuracies selected from Figures 4 and 5. As can be seen from the table, we observed the following: (1) the proposed method performs better than PLRC, which indicates that our proposed approach is effective for the large-sized video face-recognition task; (2) the AAPSRbPLRC algorithm obtains the best classification accuracy on all databases, while the APSRbPLRC algorithm achieves a sub-optimal classification performance on the Mobo database, which means that decreasing the number of images in the videos used can overcome the shortcomings of the DLRC and PLRC algorithms, and that the attention mechanism is helpful for the large-sized video face-recognition task. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Honda** & **Mobo** & **YTC** \\ \hline DCC & 96.67 & 88.54 & 61.73 \\ MMD & 97.18 & 94.10 & 67.23 \\ LEML & 97.18 & 88.69 & 50.60 \\ PML & 96.67 & 89.69 & 66.13 \\ CRG & 88.46 & 70.48 & 49.38 \\ COV & **100.0** & 90.35 & 69.18 \\ SANP & 93.60 & 93.87 & 66.70 \\ AHISD & 96.34 & 93.13 & 65.13 \\ CHISD & 95.44 & 94.21 & 63.40 \\ DLRC & 30.54 & 77.30 & 37.31 \\ PLRC-I & 65.13 & 84.67 & 49.89 \\ PLRC-II & 67.95 & 86.03 & 49.43 \\ \hline DSRbPLRC & 96.59 & 95.96 & 67.18 \\ APSRbPLRC & 96.36 & 96.83 & 68.11 \\ AAPSRbPLRC & **100.0** & **97.98** & **72.23** \\ \hline \hline \end{tabular}
\end{table}