Recently, semi-supervised learning (SSL) has attracted significant attention in machine learning fields. While numerous experimental results have shown the effectiveness of SSL methods, the theoretical analysis in this area is still poorly understood. In this paper, we investigate the generalization performance of the recently proposed sparse graph-based semi-supervised classification algorithm. We use a computationally more simple way to solve the algorithm and present the excess misclassification error bounds. In detail, the Fenchel-Legendre conjugate is first employed to reform the algorithm to an inf-sup problem. Then, the covering number is used to estimate the excess misclassification error. Experiment results are given to demonstrate the effectiveness of the sparse SSL algorithm with new solving strategy.
Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions
