复旦大学数字信号处理与传输实验室的渠入元同学的论文Regularized Recovery by Multi-order Partial Hypergraph Total Variation，被2021 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE ICASSP 2021) 录用

论文摘要：Capturing complex high-order interactions among data is an important task in many scenarios. A common way to model high-order interactions is to use hypergraphs whose topology can be mathematically represented by tensors. Existing methods use a fixed-order tensor to describe the topology of the whole hypergraph, which ignores the divergence of different-order interactions. In this work, we take this divergence into consideration, and propose a multi-order hypergraph Laplacian and the corresponding total variation. Taking this total variation as a regularization term, we can utilize the topology information contained by it to smooth the hypergraph signal. This can help distinguish different-order interactions and represent high-order interactions accurately.

论文作者：Ruyuan Qu, Jiaqi He, Hui Feng, Chongbin Xu, Bo Hu