A robust self-training algorithm based on relative node graph

Abstract

Self-training algorithm is a well-known framework of semi-supervised learning. How to select high-confidence samples is the key step for self-training algorithm. If high-confidence examples with incorrect labels are employed to train the classifier, the error will get worse during iterations. To improve the quality of high-confidence samples, a novel data editing technique termed Relative Node Graph Editing (RNGE) is put forward. Say concretely, mass estimation is used to calculate the density and peak of each sample to build a prototype tree to reveal the underlying spatial structure of the data. Then, we define the Relative Node Graph (RNG) for each sample. Finally, the mislabeled samples in the candidate high-confidence sample set are identified by hypothesis test based on RNG. Combined above, we propose a Robust Self-training Algorithm based on Relative Node Graph (STRNG), which uses RNGE to identify mislabeled samples and edit them. The experimental results show that the proposed algorithm can improve the performance of the self-training algorithm.