Matricizations and Alternating Least Squares

This lecture covers the concept of matricizations and the Alternating Least Squares method, focusing on tensor decomposition. It explains the main idea behind ALS and the application of matricizations, including canonical forms and new products like Kronecker, Khatri-Rao, and Hadamard. The lecture also discusses the ALS algorithm and the array of components in tensors, emphasizing the process of minimizing the quadratic sum. Additionally, it delves into the alignment of fibers in matrices and the vertical alignment of all fibers. The instructor provides insights on the fiber structure and the matrix alignment, highlighting the significance of these concepts in tensor analysis.