关键词: 局部约束, 自适应图, 标签传递, 半监督学习

Abstract: As an effective graph-based semi-supervised classification method, Label Propagation（LP） is widely used in image classification, text classification and other tasks. In the graph-based semi-supervised classification, the graph construction affects the performance of the algorithm to some extent. Although a large number of graph construction methods have been proposed, they exist in the problem of the separation between the graph construction and the subsequent learning processes, as well as neglecting the local structure of data. In order to solve the above problems, it proposes a new algorithm named Locality Constrained Adaptive Graph based Label Propagation（LCAGLP） in this paper. Firstly, it integrates the graph construction and label propagation into a unified framework, and also considers the locality and sparsity of samples in the process of graph construction. The idea makes the optimization graph more sparse and discriminative, which is conducive to label propagation. Then, an iterative optimization algorithm is designed for solving the objective function. Finally, extensive experiments are carried out on four databases, and the experimental results demonstrate the effectiveness of the proposed method.

Key words: locality constraint, adaptive graph, label propagation, semi-supervised learning