Graph-based semi-supervised learning via improving the quality of the graph dynamically

Abstract

Graph-based semi-supervised learning (GSSL) is an important paradigm among semi-supervised learning approaches and includes the two processes of graph construction and label inference. In most traditional GSSL methods, the two processes are completed independently. Once the graph is constructed, the result of label inference cannot be changed. Therefore, the quality of the graph directly determines the GSSL’s performance. Most traditional graph construction methods make certain assumptions about the data distribution, resulting in the quality of the graph heavily depends on the correctness of these assumptions. Therefore, it is difficult to handle complex and various data distribution for traditional graph construction methods. To overcome such issues, this paper proposes a framework named Graph-based Semi-supervised Learning via Improving the Quality of the Graph Dynamically. In it, the graph construction based on the weighted fusion of multiple clustering results and the label inference are integrated into a unified framework to achieve their mutual guidance and dynamic improvement. Moreover, the proposed framework is a general framework, and most existing GSSL methods can be embedded into it so as to improve their performance. Finally, the working mechanism, the effectiveness in improving the performance of GSSL methods and the advantage compared with other GSSL methods based on dynamic graph construction methods of the proposal are verified through systematic experiments.