On a Singular Value Decomposition of Tensors
Tensor decompositions are used for pattern recognition, exploratory data analysis, dimension reduction, and visualization of tensor-valued data. In this talk, I will review the theory, computation and visualization of the Higher-Order Orthogonal Iteration (HOOI), which is a higher-order generalization of the matrix-valued SVD. I will use a sample of resting state fMRI, and the ggbrain R package to help visualize the decomposition.