## 6 Experiment
### Use of NUMA 3D Dataset  
#### 6.2.1 Many-to-One Cross-View Action Recognition  
It is good to keep in mind that the features used here are similar to those of the IXMAS dataset. As mentioned in [40], many-to-one cross-view recognition accuracy in three cross-view scenarios can  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Test View 1** & **Test View 2** & **Test View 3** & **Test View 4** & **Test View 5** \\ \hline [PERSON] et al. [7] & 91.2 & 87.7 & 82.1 & 81.5 & 79.1 \\ [PERSON] et al. [28] & 86.1 & 81.1 & 80.1 & 83.6 & 82.8 \\ [PERSON] and [PERSON] [29] & 97.0 & 99.7 & 97.2 & 98.0 & 97.3 \\ [PERSON] and [PERSON] [29]-2 & 99.7 & 99.7 & 98.8 & 99.4 & 99.1 \\ [PERSON] et al. [8] & 98.5 & 99.1 & 99.1 & 100 & 90.3 \\ [PERSON] and [PERSON] [44] & 76.7 & 73.3 & 72.0 & 73.0 & N/A \\ [PERSON] et al. [25] & 86.7 & 89.9 & 86.4 & 87.6 & 66.4 \\ Our supervised method & 100 & 99.7 & 99.5 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Many-to-one cross-view action recognition results on the IXMAS dataset, where each column corresponds to a test view.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Test View 0** & **Test View 1** & **Test View 2** & **Test View 3** & **Test View 4** \\ \hline Training view 0 & (79.6, 92.1, 99.4, 82.4, 72.1, **100**) & 79.4, 86.1, **99.7**) & NA & (79.8, 94.9, 91.2, 85.8, 77.3, **100**) & 71.5, 62.7, 99.7 \\ \hline Training view 1 & (82.0, 83.0, 87.3, 57.1, 48.8, **99.7**) & (68.3, 70.6, 87.8, 48.5, 40.9, **100**) & 74.0, 89.7, 92.1, 71.1, 83.7, 90.0, 51.2, 49.4, **100**) & NA \\ \hline Training view 2 & (73.0, 97.0, 87.6, 82.4, **100**) & (74.1, 94.2, 98.2, 80.9, 79.7, **100**) & 74.0, 96.7, 99.4, 82.7, 70.9, **100**) & NA & (66.9, 83.9, 95.4, **44.2**, 37.9, **100**) \\ \hline Training view 3 & (81.2, 97.3, 97.8, 95.5, 90.6, **100**) & NA & (75.8, 96.4, 91.2, 77.6, 79.7, **99.7**) & (78.0, 89.7, 78.4, 40.9, **93.6**, **99.7**) \\ \hline Training view 4 & NA & (79.9, 96.7, 99.1, 92.7, 94.8, **99.7**) & (76.8, 97.9, 90.9, 84.2, 69.1, **99.7**) & (74.8, 97.6, 88.7, 48.9, 95.5, 83.9, **98.9**) & (74.8, 84.9, 95.9, **94.2**, 39.1, **99.4**) \\ \hline Average & (79.0, 94.4, 93.0, 79.4, 74.5, **99.9**) & (74.7, 87.8, 95.6, 75.4, **99.9**) & (75.2, 95.1, 93.4, 76.4, 91.2, 87.1, **79.1**) & (71.2, 84.8, 95.1, **44.2**, 37.9, **100**) \\ \hline \hline \end{tabular}
\end{table}
Table 3: One-to-one cross-view recognition results of various unsupervised approaches on the IXMAS dataset. The results in brackets are the recognition accuracies of [8; 27; 28; 29; 43], and our unsupervised approach, respectively.  
be expressed as cross-camera view, cross-subject, and cross-environment. Following [40], our approach is compared with [31; 40; 45; 46; 47].  
Table 5 shows that our approach is better than [40] Low-resolution visual features (LowR) by 10.4% and 3.9% in cross-environment and cross-view scenario, respectively. Furthermore, in the cross-subject scenario, it reaches an incredible performance with LowR added to [40]. A close look at the different comparison shows that the most important gain of performance of Seb1 in cross-environment, cross-subject, and cross-view scenarios are, respectively, 62.3% (over [48]), 30.4% (over [48]), and 32.0% (over [31]). This shows an incredible gain of performance obtained by the Seb1 approach due to the use of SAM to determine similarities of samples in different views and shared and private facts for modeling cross-view data.