关键词: 自训练, 密度峰值, 切边权值, 假设检验

Abstract:

In view of the influence of mislabeled samples on the performance of self-training algorithm in the process of iteration, a self-training algorithm based on density peak and cut edge weight is proposed. Firstly, the representative unlabeled samples are selected for labels prediction by space structure, which is discovered by clustering method based on density of data. Secondly, cut edge weight is used as statistics to make hypothesis testing. This technique is for identifying whether samples are labeled correctly. And then the set of labeled data is gradually enlarged until all unlabeled samples are labeled. The proposed method not only makes full use of space structure information, but also solves the problem that some data may be classified incorrectly. Thus, the classification accuracy of algorithm is improved in a great measure. Extensive experiments on real datasets clearly illustrate the effectiveness of proposed method.

Key words: self-training, density peak, cut edge weight, hypothesis testing