On optimal low-rank approximation of multidimensional discrete signals

This brief describes an algorithmic development of the optimal low-rank approximation (LRA) of multidimensional (M-D) signals with M⩾3. The algorithms developed can be regarded as a dimensional generalization of the singular value decomposition (SVD) which is of fundamental importance for analyzing signals that can be represented in a matrix form. In particular, iterative algorithms for optimal and suboptimal LRA of three-dimensional (3-D) arrays are presented in detail. Application of the 3-D LRA to the compression of image sequences is discussed