Semi-supervised One-pass Learning under Distribution Shift
Hui Xie, Xuanxuan Liu, Li Guo · 2023
One-pass learning is one of the most popular and widely used online learning method for high-speed and large-scale streaming data. However, large-scale data usually has the problem of label scarcity, and the insufficient labeled data has a great negative impact on the performance of one-pass learning. The existing research on semi-supervised online learning has difficulty extending to one-pass learning due to the iterative nature of semi-supervised methods. In addition, the distribution of complex streaming data may also change over time. Just directly ignoring the change in data distribution will seriously affect the value and significance of learning. Therefore, one-pass learning under distribution shift when labels are scarce is a challenging task. In this paper, we propose a semi-supervised one-pass learning method with forgetting factor to handle the change of data distribution efficiently. In this method, we use an improved mini-batch label propagation algorithm to obtain pseudo-labels for the incoming unlabeled data. The forgetting factor is introduced to calculate the memory matrix for the labeled data and the pseudo-labeled data to deal with the data distribution change. We also introduce a semi-supervised confidence factor to balance the weight of labeled and pseudo-labeled data. Extensive experiments on streaming datasets demonstrate the effectiveness of our approach. Especially under the one-pass constraint, it has achieved far better performance than the existing semi-supervised online learning.