*Scaling Subspace-Driven Approaches Using Information Fusion DOI: http://dx.doi.org/10.5772/intechopen.109946*

inadequate training, e.g., insufficient data or non-convergence. This can also be considered as augmenting the training data with new information or a new view for one modality which might not necessarily contained in the testing or the validation data. Moreover, we repeat the same experiment with the distortion of two modalities before learning the sparse coefficient matrices for both DMSC and DRoGSuRe. The results for the ARL dataset are depicted in **Tables 3** and **4**, while results for the EYB dataset are shown in **Tables 5** and **6**. For ARL dataset, we refer to Visible, S0, S1, S2 and DoLP as Mod 0, 1, 2, 3 and 4 respectively. For EYB Dataset, we refer to Face, left eye, nose, mouth and right eye as mod 0, 1, 2, 3, and 4. We refer to each modality as Mod, where L denotes learning and V denotes validation results. From the results, it is clear that DRoGSuRe is showing a significant improvement in the clustering accuracy as compared to DMSC for both learning and validation set. The reason for that, is again, due to the fact that perturbing one or two modalities would have less impact on the overall performance for DRoGSuRe in comparison to DMSC.
