## 3 Proposed Method
### SRPLC-K-Means  
The prerequisite for achieving subdomain adaptation is that the label of the target domain is known. However, the target domain is an unlabeled set consisting of feature vectors. Some scholars have used pseudo labels to mark the target domain [18], but the model that marks pseudo labels is obtained through suboptimal training [28]. However, the accuracy of pseudo labels greatly affects the model's alignment of data distribution in different fields. The existing noise in the samples and distribution discrepancies in the original data are the reasons why the accuracy is poor. Filtering noisy samples and correcting erroneous pseudo labels are necessary for unsupervised domain adaptation. Therefore, this article proposes the SRPLC-K-means method to obtain the higher confident pseudo labels. SRPLC-K-means requires the use of K-means for clustering and arranging the distance between the samples and cluster centers. K-means clustering is an unsupervised clustering method that measures the distance between the samples using the Euclidean distance. The flowchart of SRPLC K-means is shown in Figure 3, and its process is as follows:  
1. Randomly select \(k\) initial cluster centers \((z_{1},z_{2},\ldots,z_{k})\) from \(N\) samples;
2. Calculate the Euclidean distance from each sample \(x^{s}\) to the nearest cluster center \(z_{j}\) and assign the sample \(x^{s}\) to the cluster where \(z_{j}\) is located. The calculation of the Euclidean distance from the center is shown in Equation (14); \[d=\sqrt{\sum_{i=1}^{n}{(x_{i}^{s}-x_{i}^{z})}^{2}}\] (14) where \(x_{i}^{z}\) is the coordinates of the cluster center, \(x_{i}^{s}\) is the coordinates of the sample, and \(n\) is the feature dimension of the sample;
3. Update the cluster center and recalculate the distance between each sample and the cluster center it belongs to;
4. Repeat steps 2 and 3 until the sum of the Euclidean distances from all the samples to their respective cluster centers converges to a fixed value;
5. Arrange the samples of each cluster in ascending order based on their Euclidean distance from the cluster center they belong to;
6. Reserve the samples in the top \(d\)% of the sequence number. Adopting the idea of the minority obeying the majority, modify the pseudo labels of minority classes in the new sample set to those of majority classes.  
The pseudo labels are marked by classifier labels trained on source domain data, so they are mainly affected by discrepancies in data distribution and noise. The classification using K-means clustering is mainly affected by noise. When using K-means clustering  
Figure 3: The flowchart of the SRPLC-K-means method.  
for classification, the noise of the samples near the cluster center is low, so that the labels of the samples have higher confidence. Edge samples are prone to category aliasing and misjudging due to their heavily existing noise and being far from the cluster center. Therefore, this article proposes filtering and correction operations to remove the samples affected by noise and correct the false labels caused by the discrepancies in the data distribution. As shown in Figure 4, red and yellow represent the source domain and target domain samples, respectively. The numbers marked on the samples represent their pseudo labels, and the black cross symbol represents the cluster center of the target domain. The samples inside the red circle are those that are far from the cluster center of the target domain and have been mislabeled. They contain significant noise and are close to other categories, rendering them difficult to distinguish and should be filtered. The samples inside the green circle are those that are close to the cluster center of the target domain but have been mislabeled. They contain less noise, but their data distribution is similar to other categories in the source domain. Due to the small number of samples in the target domain, its pseudo labels should be corrected.