Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel $k$-means, and Heat Kernel. (arXiv:2203.09888v3 [cs.LG] UPDATED)

We propose a theoretical framework of multi-way similarity to model
real-valued data into hypergraphs for clustering via spectral embedding. For
graph cut based spectral clustering, it is common to model real-valued data
into graph by modeling pairwise similarities using kernel function. This is
because the kernel function has a theoretical connection to the graph cut. For
problems where using multi-way similarities are more suitable than pairwise
ones, it is natural to model as a hypergraph, which is generalization of a …

arxiv embedding heat hypergraph kernel lg modeling