# UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
## 7 Running Time  
Figure 5 depicts the results of the running time of UFODMV and the benchmark methods. The running time of all methods increased when the data were sparse. For example, all methods applied to the Fox News dataset required higher running time than when they were applied to the Caltech-7 and Handwritten datasets. Additionally, the running time of all the methods except for SPEC increased as the number of selected features increased. This is because SPEC returns a weighted vector of all features. Therefore, for each dataset, it has a fixed running time for different numbers of selected features. For the Handwritten dataset, as shown in Figure 4(a), UFODMV has the best running time compared with OMVFS, UFSSF and SPEC. UFODMV selected different numbers of features with excellent running time. It selected 25, 50, and 75 features in approximately 0.023 s. Additionally, UFODMV took 0.875 s to select 100 features. This is because of two factors: (1) when a new chunk arrives, UFODMV re-partitions only those clusters whose features exceed the predefined threshold T, not all the clusters; and (2) in the merging step, distance is computed between centroids so as to merge the closest clusters. This is unlike other clustering methods that merge clusters based on the distance between their features. SPEC took the highest running time compared to the benchmarked methods, returning a weight vector of features in approximately 19.56 s. OMVFS and UFSSF were the second and third fastest methods, respectively. They were very competitive when 25 features were selected. However, they consistently recorded a difference of around 0.52% for all other numbers of selected features. However, with the Caltech and Fox News datasets, UFODMV was not as fast as the benchmark methods (see Figure 4(b),c). This is due to two factors: (1) the UFODMV algorithm has many for-loop functions in order to incrementally cluster the instances after clustering the views. Actually, it is well-known that the Matlab platform works very slowly with loops, which is why a cloud platform was developed for the loop function. (2) If a new chunk arrives, UFODMV assigns the new values of existing features into their corresponding clusters. Then, UFODMV re-clusters current clusters and merges them if they exceed a pre-set radius threshold. However, for sparse datasets, such as those of Caltech-7 and Fox News, we need to set a small radius value in order to obtain the required number of selected features. However, the negative side of this is that the smaller the radius, the more clusters need to be clustered and sequentially merged, which in turn requires more processing time. SPEC was the second-highest method in terms of running time for both datasets. It needed 32.68 and 36.04 s for the Caltech-7 and Fox datasets, respectively. OMVFS is the fastest in terms of running time for both datasets. This is because it uses matrix factorisation, which can process sparse data efficiently. UFSSF is the second method with the lowest running time for both datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method\textbackslash Number of Selected Features** & **25** & **50** & **75** & **100** & **Metric** \\ \hline UFODMV (proposed) & **0.586** & **0.651** & **0.662** & **0.676** & \\ \hline OMVFS & 0.287 & 0.302 & 0.302 & 0.309 & \\ \hline SPEC & 0.503 & 0.573 & 0.599 & 0.612 & \\ \hline UFSSF & 0.508 & 0.586 & 0.602 & 0.625 & \\ \hline UFODMV (proposed) & **0.598** & **0.664** & **0.677** & **0.688** & \\ \hline OMVFS & 0.288 & 0.296 & 0.307 & 0.311 & \\ \hline SPEC & 0.501 & 0.581 & 0.584 & 0.599 & \\ \hline UFSSF & 0.508 & 0.585 & 0.617 & 0.627 & \\ \hline UFODMV (proposed) & **0.591** & **0.654** & **0.666** & **0.676** & \\ \hline OMVFS & 0.287 & 0.298 & 0.304 & 0.309 & \\ \hline SPEC & 0.5 & 0.579 & 0.584 & 0.599 & \\ \hline UFSSF & 0.508 & 0.585 & 0.606 & 0.625 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification accuracy using Fox News dataset with IB1.