Abstract:

According to semisupervised learning, both labeled and unlabeled data are used to train a classifier. Traditional semi supervised training methods do not distinguish the noise in the samples. Because of the noisy samples, this kind of method may impact the training process, and then affect the classifier results. To solve the problem, a kind of semisupervised training approach based on RSC model and noise removing is proposed. The noise removing function is added to the traditional approach while training the unlabeled samples. The experiments show that this kind of algorithm can both improve the classification accuracy and make the algorithm more stable.

Key words: semi-supervised learning;noise remove;classifier training;RSC model;label extension;training set1