Tensor ring decompositions offer a powerful framework for analyzing multidimensional data. These decompositions represent tensors as a sum of rank-1 or low-rank matrices, leading to significant computational advantages over traditional tensor representations. By exploiting the inherent structure of multiway data, tensor ring decompositions enable a… Read More

Tensor ring decomposition offers a novel approach to data representation by decomposing high-order tensors into a sum of low-rank matrices. This factorization exploits the inherent structure within data, enabling efficient storage and processing. Applications range from recommender systems to natural language processing, where tensor decomposition … Read More