关键词: 半监督算法, 图方法, 相对变换

Abstract: In order to enhance the ability of dealing with sparse and noisy data of graph-based semi-supervised learning algorithm Local and Global Consistency (LGC), a LGC algorithm based on relative transformation was proposed. The original data space was converted to the relative data space by the proposed algorithm. In the relative space, the noise and outliers would become further away from the normal points, and the sparse points would become relative closer, which can improve the performance of the LGC algorithm. The experimental results on several data sets show that the classification capability of the proposed algorithm for noisy and sparse data sets increases significantly.

Key words: semi-supervised learning, graph method, relative transformation