Higher-order singular value decomposition

Higher-order singular value decomposition (HOSVD) is an extension of the traditional singular value decomposition (SVD) to tensor data, which are multi-dimensional generalizations of matrices. While a matrix is a two-dimensional array (with rows and columns), a tensor can have three or more dimensions, commonly referred to as modes.