Tensor ring decomposition presents a powerful framework for analyzing high-order data. This read more technique factorizes complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Thus, tensor ring decomposition enables efficient storage and computation with large datasets by exploiting the underlying

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