  Recently, pseudo label based semi-supervised learning has achieved great
success in many fields. The core idea of the pseudo label based semi-supervised
learning algorithm is to use the model trained on the labeled data to generate
pseudo labels on the unlabeled data, and then train a model to fit the
previously generated pseudo labels. We give a theory analysis for why pseudo
label based semi-supervised learning is effective in this paper. We mainly
compare the generalization error of the model trained under two settings: (1)
There are N labeled data. (2) There are N unlabeled data and a suitable initial
model. Our analysis shows that, firstly, when the amount of unlabeled data
tends to infinity, the pseudo label based semi-supervised learning algorithm
can obtain model which have the same generalization error upper bound as model
obtained by normally training in the condition of the amount of labeled data
tends to infinity. More importantly, we prove that when the amount of unlabeled
data is large enough, the generalization error upper bound of the model
obtained by pseudo label based semi-supervised learning algorithm can converge
to the optimal upper bound with linear convergence rate. We also give the lower
bound on sampling complexity to achieve linear convergence rate. Our analysis
contributes to understanding the empirical successes of pseudo label-based
semi-supervised learning.

Machine Learning;Artificial Intelligence