Robust self-tuning semi-supervised learning

Abstract

We investigate the issue of graph-based semi-supervised learning ( SSL). The labeled and unlabeled data points are represented as vertices in an undirected weighted neighborhood graph, with the edge weights encoding the pairwise similarities between data objects in the same neighborhood. The SSL problem can be then formulated as a regularization problem on this graph. In this paper we propose a robust self-tuning graph-based SSL method, which (1) can determine the similarities between pairwise data points automatically; (2) is not sensitive to outliers. Promising experimental results are given for both synthetic and real data sets.