A new method of distance measure for graph-based semi-supervised learning

With an intensive study of the existing density-sensitive distance measures, we proposed a new distance measure for graph-based semi-supervised learning. The proposed measure can not only effectively amplify the distance between data points in different high-density regions, but also reduce the distance among data points in a same high-density region. Then, a graph-based semi-supervised clustering algorithm is presented based on the proposed distance measure. Experimental results on some UCI data sets show that the proposed method has obvious advantages than the old one.