Figure 1. Detailed workflow to determine the optimal parameter set.

First, we construct a graph for regularization with only labeled samples by varying two parameters. In this phase, we use k-fold cross validation to determine the optimal parameter set. We then apply semi-supervised learning with the obtained optimal parameter set and predict the labels of the unknown samples. The proposed method uses unlabeled sample information to build a classifier by iterating the procedure.