Robust Semisupervised Graph Classifier Learning With Negative Edge Weights

In a semisupervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier in order to assign labels to unclassified samples. In this paper, we construct a complete graph-based binary classifier given only samples' feature vectors and partial labels. Specifically, we first build appropriate similarity graphs with positive and negative edge weights con...