Sampling Method for Generalized Graph Signals With Pre-Selected Vertices via DC Optimization
The work proposes a vertex-wise flexible sampling method for graph signals that incorporates prior knowledge about specific vertices. It formulates this constrained selection problem as a difference-of-convex optimization problem, solved using a double-proximal gradient algorithm. Experiments show the method achieves superior recovery accuracy compared to existing techniques on various graph signal models.