In this way, we can use only a small portion of labeled data to predict the remaining unlabeled data effectively (Zhou et al., 2003).
with the tensor embeddings, we first find the K nearest neighbors of each data point, and build a similarity graph G. We then form an affinity matrix W , where W ij = 1 if i and j are connected, otherwise, W ij = 0.