Constrained Local and Global Consistency for semi-supervised learning

One of the widely used algorithms for graph-based semi-supervised learning (SSL) is the Local and Global Consistency (LGC). Such an algorithm can be viewed as a convex optimization problem that balances fitness on labeled examples and smoothness on the graph using a graph Laplacian. In this paper, we provide a novel graph-based SSL algorithm incorporating two normalization constraints into the regularization framework of LGC. We prove that our method has closed-form solution and generalizes two existing methods, being more flexible than the original ones. Through experiments on benchmark data sets, we show the effectiveness of our method, which consistently outperforms the competing methods.