## 3 Materials and Methods
### Experimental Methods
In this case, to identify the less-beneficial domains in each iteration, LSA-MMFT utilizes a label similarity analysis based on the STS approach to evaluate the transferability between the SD and the TD. Moreover, once a single less-beneficial domain is removed, the remaining domains are assigned the roles of SDs in each iteration, and then LSA-MMFT performs the distribution of means alignment for both the SDs and the TD in each iteration. Tangent space features are then extracted from the SDs and the TD, respectively. The extracted features are then utilized for Grassmann manifold feature learning, followed by the MMFT classifier, utilized to predict the TD [11]. This process is repeated in each iteration until two domains remain; then, the best performance across each iteration is considered. Moreover, whenever a new domain is assigned the role of TD, the above-mentioned steps are repeated. Furthermore, ROD first identifies the number of domains available when it is employed to perform source selection, and each domain takes a turn being the TD. Therefore, whenever a domain is selected to be the TD, ROD iterates through all the source domains, removing a single less-beneficial domain in each iteration until only two SDs remain. Notably, to identify the less-beneficial domains in each iteration, ROD ranks the similarities between the SDs and the TD; this is achieved by computing the symmetrized \(KL\) divergence-weighted average of the principal angles [32]. However, once a single low-beneficial domain has been identified and removed, the remaining SDs, including the assigned TD, are utilized to perform the distribution of means alignment in each iteration. This is followed by the extraction of tangent space features from the SDs and the TD, respectively. In this case, the extracted features are then used for Grassmann manifold feature learning, followed by the MMFT classifier, utilized to predict the TD. This process is repeated in each iteration until two domains remain; then, the best performance across each iteration is considered. Moreover, whenever a new domain is assigned the role of TD, the above-mentioned steps are repeated. When DTE is employed, the transferability between the SD and the TD is evaluated by linking the distribution information with the between-class scatter matrix in the SD and the between-domain scatter matrix between the SD and the TD [10]. As such, DTE iterates through the nine domains, and each of the nine domains takes a turn being the TD. In this instance, a single less-beneficial domain is discarded in each iteration when the transferability between the SD and the TD is evaluated, while the remaining domains are assigned the roles of SDs, utilized to enhance the prediction rate in the TD. The same processing techniques applied when DTE and LSA-MMFT are employed to perform transfer mapping after source selection in each iteration are utilized when ROD is applied. In this case, the process of identifying and discarding a single less-beneficial domain is repeated in each iteration until two domains remain; then, the best performance across each iteration is considered. Notably, whenever a new domain is assigned the role of TD, the above-mentioned process is repeated.