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

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3 Author(s)
Yuan-Dong Lan ; School of Computer Science and Engineering, South China University of Technology, Guangzhou, China ; Huifang Deng ; Tao Chen

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.

Published in:

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on  (Volume:4 )

Date of Conference:

10-13 July 2011