# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Experiment Results and Analysis
For F-score, our proposed method exceeds the second best method by 1.41%, 14.45%, 4.39%, 3.89%, 6.55%, 11.23%, 3.75%, 7.75% and 0.63% for the corresponding data sets, respectively. In terms of ARI, our proposed method exceeds the second best method by 0.84%, 14.59%, 5.31%, 7.37%, 6.97%, 14.33%, 12.11%, 2.54% and 0.16% for the corresponding data set, respectively. The above results demonstrate the effectiveness and superiority of the proposed method. Hence, our LGOMSC method is a valuable multi-view subspace clustering method. From the results in these tables, one can see that the baseline method (i.e., FeatConcate) sometimes exhibits comparable performance to the multi-view subspace method and even exceeds some multi-view clustering methods. However, in most cases, this baseline method still has a big gap in comparison with multi-view clustering methods. It confirms that multi-view clustering methods that consider the consistency or complementary information can obtain a good multi-view clustering performance. However, in some data sets, e.g., 3 sources and 100 leaves, the multi-view k-means method RMKMC, produces even worse results than FeatConcate. This phenomenon has been observed by some previous researchers [18; 54]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ARI (\%)** & **3 sources** & **100 leaves** & **BBC** & **Caltech101** & **COIL-20** & **NottingHill** & **Webbkb** & **Cornell** & **Wikipedia** \\ \hline FeatConcate & 56.90 & 63.17 & 49.20 & 41.94 & 58.89 & 85.67 & 76.99 & 12.33 & 44.28 \\ Co-reg\_c & 58.38 & 74.20 & 6.05 & 27.03 & 66.46 & 64.70 & 16.20 & 6.57 & 18.39 \\ Co-reg\_p & 54.81 & 69.36 & 1.52 & 26.46 & 68.03 & 60.71 & 27.19 & 2.47 & 2.80 \\ LMSC & 56.72 & 68.41 & 69.70 & 37.80 & 69.74 & 78.20 & 80.79 & 10.18 & 42.89 \\ FMR & 53.70 & 58.66 & 69.13 & 31.86 & 64.83 & 64.68 & 72.84 & 12.01 & 42.12 \\ MLRSSC & 0.33 & 0.12 & \(-\)0.04 & 0.93 & 0.03 & 0.04 & \(-\)0.14 & 1.33 & \(-\)0.09 \\ RMKMC & 34.27 & 0.00 & 45.61 & 41.21 & 56.45 & 64.69 & 73.61 & 15.73 & 45.97 \\ mPAC & 69.79 & 35.87 & 40.27 & 45.19 & 67.08 & 84.79 & 28.17 & 8.97 & 38.86 \\ LMVSC & 43.16 & 61.36 & 71.75 & 34.48 & 67.67 & 81.15 & 80.81 & 32.54 & 44.24 \\ PMSC & 36.70 & 17.47 & 1.17 & 17.58 & 46.33 & 57.26 & 0.18 & 10.44 & 1.96 \\ COMVSC & 36.94 & 62.55 & 47.36 & 50.17 & 70.28 & 74.50 & 25.52 & 17.68 & 44.69 \\ GMC & 32.87 & 65.86 & 47.46 & 39.64 & 84.45 & 2.21 & 1.02 & 3.47 & 6.07 \\ Ours & 70.63 & 88.79 & 77.06 & 57.54 & 91.42 & 100.00 & 92.92 & 35.08 & 46.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The clustering performance comparison in terms of ARI on nine multi-view data sets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**F-score (\%)** & **3 sources** & **100 leaves** & **BBC** & **Caltech101** & **COIL-20** & **NottingHill** & **Webbkb** & **Cornell** & **Wikipedia** \\ \hline FeatConcate & 66.61 & 63.54 & 60.32 & 56.91 & 61.05 & 88.77 & 92.78 & 33.27 & 50.34 \\ Co-reg\_c & 69.04 & 74.46 & 37.00 & 42.83 & 68.20 & 72.38 & 79.66 & 32.06 & 26.96 \\ Co-reg\_p & 66.01 & 69.67 & 37.74 & 42.34 & 69.67 & 69.08 & 81.71 & 31.12 & 13.00 \\ LMSC & 65.58 & 68.72 & 76.81 & 53.58 & 71.33 & 83.04 & 93.02 & 42.59 & 48.96 \\ FMR & 63.89 & 59.07 & 76.55 & 47.24 & 66.58 & 72.19 & 90.08 & 34.50 & 48.22 \\ MLRSSC & 37.49 & 1.97 & 37.88 & 55.93 & 9.42 & 36.03 & 79.27 & 42.88 & 19.57 \\ RMKMC & 47.89 & 1.86 & 57.46 & 55.64 & 58.84 & 72.58 & 91.87 & 35.94 & 51.85 \\ mPAC & 76.93 & 36.61 & 59.10 & 61.19 & 68.73 & 88.17 & 79.18 & 43.34 & 45.81 \\ LMVSC & 55.85 & 61.67 & 78.44 & 51.90 & 69.29 & 85.36 & 93.83 & 49.57 & 50.13 \\ PMSC & 57.05 & 18.77 & 38.48 & 43.46 & 49.56 & 66.84 & 79.27 & 43.55 & 19.67 \\ COMVSC & 56.74 & 62.95 & 62.72 & 71.83 & 71.79 & 80.11 & 81.47 & 46.18 & 51.02 \\ GMC & 52.88 & 66.27 & 63.05 & 61.54 & 85.31 & 36.94 & 78.67 & 37.06 & 23.00 \\ Ours & 78.34 & 88.91 & 82.83 & 75.72 & 91.86 & 100.00 & 97.58 &Multi-view subspace clustering based on intact space learning methods like LMSC and FMR performs clustering on the latent representation space.