A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
This work addresses semi-supervised learning for regression, offering a simple algorithm with theoretical improvements, though it appears incremental as it builds on existing integral operator methods.
The authors tackled semi-supervised regression by using top eigenfunctions from an integral operator as basis functions and learning via linear regression, achieving an improved error bound compared to supervised learning, with empirical verification.
In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.