  We analyze the convergence of the weighted nonlocal Laplacian (WNLL) on high
dimensional randomly distributed data. The analysis reveals the importance of
the scaling weight $\mu \sim P|/|S|$ with $|P|$ and $|S|$ be the number of
entire and labeled data, respectively. The result gives a theoretical
foundation of WNLL for high dimensional data interpolation.
