Noise-robust graph-based semi-supervised learning with dynamic shaving label propagation

Abstract

Graph-based semi-supervised classification is widely used because it effectively exploits the characteristics of unlabeled data. However, the existing methods have a drawback in that they do not account for the inherent noise of the data. Noise in graph data refers to nodes that are isolated from classes and overlapping nodes between different classes. Therefore, neglecting noise can distort the data manifold, leading to over-smoothing and overall performance degradation. In this paper, we propose a noise-robust model called the dynamic shaving label propagation algorithm. Our proposed method comprises three parts: graph construction, noise definition and cutting, and label propagation. First, in the graph construction process, k is determined at the point when reverse nearest neighbors are identified for the most isolated nodes. Second, the noise definition process identifies the nodes classified as noise based on the number and distance of their reverse nearest neighbors. Finally, label propagation is performed dynamically and iteratively by adjusting the noise removal intensity. Using various simulated and real-world datasets, we evaluate the accuracy and noise robustness of the proposed method with those of existing methods to evaluate its effectiveness and applicability. The comparison results demonstrate that the proposed method outperforms the existing alternatives.