Graph Construction Based on Re-weighted Sparse Representation for Semi-supervised Learning

摘要

Graph construction is the key part of graph-based semi-supervised learning algorithms, and the performance of algorithms depends heavily on the graph weight matrix given by graph construction process. In this paper, we propose a modified L1 graph construction model based on the distance of sample pairs. The solutions of the proposed method include the distance of sample pairs to improve the accuracy. Numerical experiments on face dataaets indicate that the results yielded by the proposed algorithm perform better than the algorithms based on traditional graph construction methods and L1 graph.