SILO: Generalized Tensor Decompositions: Algorithms and Applications
Abstract: Tensor decompositions generalize matrix decompositions from matrix data (i.e., 2-D arrays) to tensor data (i.e., N-D arrays) and are a fundamental technique for uncovering low-dimensional structure in high-dimensional datasets, with applications across all of science and engineering. Conventional tensor decompositions seek low-rank tensors that best fit the data with …