Mutual k-Nearest Neighbor Graphs for Semi-Supervised Learning
Kohei Ozaki (0951027)
In graph-based semi-supervised learning, graph construction methods are
known to influence performance and have gained increasing attention
over the past few years. In this thesis, we investigate
the effect of the hub nodes of a graph on classification tasks and
propose a new efficient graph construction approach.
In natural language
processing tasks, such as word sense disambiguation and document
classification,
we demonstrate that our proposed method outperforms the state-of-the-art
graph construction methods in terms of prediction accuracy in most
cases.
In addition, we show the graph produced by our proposed method
satisfies the cluster assumption better than other methods.