Laplacian Learning

Laplacian learning is a graph-based semi-supervised learning technique aiming to infer labels for unlabeled data points by leveraging the structure of the underlying data graph, often represented by its Laplacian matrix. Current research focuses on extending Laplacian learning to quantum computing, enhancing robustness against noise and adversarial attacks (e.g., in DeepFake detection), and improving its performance in low-label scenarios through manifold optimization and novel sampling strategies within graph neural networks and other models. These advancements are significant for various applications, including improving the efficiency and accuracy of semi-supervised learning across diverse domains such as image classification, video analysis, and network anomaly detection.