复旦大学数字信号处理与传输实验室的盛航同学的论文Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals，被IEEE Transactions on Signal and Information Processing over Networks录用

论文摘要：Finite time-vertex graph signals (FTVGS) provide an efficient representation for capturing spatio-temporal correlations across multiple data sources on irregular structures. Although sampling and  reconstruction of FTVGS with known spectral support have been extensively studied, the case of unknown spectral support requires  further investigation. Existing random sampling methods may extract samples from any vertex at any time, but such strategies are not  friendly in practice, where sampling is typically limited to a subset of  vertices and moments. To address this requirement, we propose a subset random sampling scheme for FTVGS. Specifically, we first randomly select a subset of rows and columns to form a submatrix, followed by random  sampling within that submatrix. In theory, we provide sufficient conditions for reconstructing the original FTVGS with high probability. Additionally, we introduce a reconstruction framework incorporating low-rank, sparsity, and smoothness priors (LSSP), and verify the feasibility of the reconstruction and the effectiveness of the framework through experiments.

论文作者：Hang Sheng, Qinji Shu, Hui Feng, Bo Hu