Centered kNN Graph for Semi-Supervised Learning

Ikumi Suzuki, Kazuo Hara

Abstract: Graph construction is an important process in graph-based semi-supervised learning. Presently, the mutual kNN graph is the most preferred as it reduces hub nodes which can be a cause of failure during the process of label propagation. However, the mutual kNN graph, which is usually very sparse, suffers from over sparsification problem. That is, although the number of edges connecting nodes that have different labels decreases in the mutual kNN graph, the number of edges connecting nodes that have the same labels also reduces. In addition, over sparsification can produce a disconnected graph, which is not desirable for label propagation. So we present a new graph construction method, the centered kNN graph, which not only reduces hub nodes but also avoids the over sparsification problem.