Performance analysis of the LapRSSLG algorithm in learning theory

Abstract

It is known that one aim of semi-supervised learning is to improve the prediction performance using a few labeled data with a large set of unlabeled data. Recently, a Laplacian regularized semi-supervised learning gradient (LapRSSLG) algorithm associated with data adjacency graph edge weights is proposed in the literature. The algorithm receives success in applications, but there is no theory on the performance analysis. In this paper, an explicit learning rate estimate for the algorithm is provided, which shows that the convergence is indeed controlled by the unlabeled data.