Algorithm 1 Proposed semi-supervised learning for localization

Step 1: Collect labeled training data set ${\{(x_i,y_i)\}}_{i=1}^l$ and unlabeled data set ${\{x_j\}}_{j=1}^u$ in a time-series. Step 2: Obtain kernel matrix K in (4), normalized Laplacian matrix L in (5) and matrix $\mathsf{\Lambda }$ in (10). Step 3: Choose values of ${\mu }_1$ and ${\mu }_2$ in (18), and then calculate the pseudolabels in (18). Step 4: Choose values of C and $C^{*}$, and then solve the linear equation in (20). Step 5: Based on the optimal solution ${\alpha }^{*}$ and $b^{*}$ from Step 4, builds the localization model in (2).