Lars Eldén: Generalizing Spectral Graph Partitioning to Sparse Tensors

Spectral graph partitioning is a method for clustering data organized as undirected graphs. The method is based on the computation of eigenvectors of the graph Laplacian. In many areas one wants to cluster data from a sequence of graphs. Such data can be organized as large sparse tensors. We present a spectral method for tensor partitioning based on the computation of a best multilinear rank aproximation of the tensor. A few applications are briefly discussed.