**4.5 Testing with limited noisy testing data**

In the following, we study the effect of using noiseless data for training while validating with noisy and missing data. We add Gaussian noise to one data modality in the validation set and vary the SNR by varying the noise variance. We subsequently assume that we only have one modality available at testing. Then, we keep increasing the number of available noiseless data modalities beside the noisy modality. We

**Figure 5.** *ARL noiseless training and validating on limited noisy data.*

**Figure 6.** *EYB noiseless training and validating on limited noisy data.*

average the results considering all different combinations of data modalities for ARL and EYB datasets. The results are depicted in **Figures 5** and **6** respectively. For the ARL dataset, we note the increasing gap between DMSC and DRoGSuRe as we augment the sensing capacity with noise-free modalities. On the other hand, for the EYB dataset and at lower SNR, the performance of DRoGSuRe is slightly worse than DMSC which might be explained by the results in **Table 2**; as the training accuracy for DMSC is slightly better than DRoGSuRe in the case of clean training. However, at higher SNR, the performance of the two approaches is very close.
